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Engineering - Computational Intelligence and Complexity | Constraint Programming and Decision Making

Constraint Programming and Decision Making

Ceberio, Martine, Kreinovich, Vladik (Eds.)

2014, XII, 209 p. 33 illus.

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  • Presents extended versions of selected papers from the recent annual International Workshops on Constraint Programming and Decision Making held in the US (El Paso, Texas), in Europe (Lyon, France), and in Asia (Novosibirsk, Russia) from 2008 to 2012
  • The presented research deals with all stages of decision making under constraints: (1) formulating the problem of multi-criteria decision making in precise terms, (2) determining when the corresponding decision problem is algorithmically solvable; (3) finding the corresponding algorithms, and making these algorithms as efficient as possible and (4) taking into account interval, probabilistic and fuzzy uncertainty inherent in the corresponding decision making problems
  • Written by experts in the field

In many application areas, it is necessary to make effective decisions under constraints. Several area-specific techniques are known for such decision problems; however, because these techniques are area-specific, it is not easy to apply each technique to other applications areas. Cross-fertilization between different application areas is one of the main objectives of the annual International Workshops on Constraint Programming and Decision Making. Those workshops, held in the US (El Paso, Texas), in Europe (Lyon, France), and in Asia (Novosibirsk, Russia), from 2008 to 2012, have attracted researchers and practitioners from all over the world. This volume presents extended versions of selected papers from those workshops. These papers deal with all stages of decision making under constraints: (1) formulating the problem of multi-criteria decision making in precise terms, (2) determining when the corresponding decision problem is algorithmically solvable; (3) finding the corresponding algorithms, and making these algorithms as efficient as possible; and (4) taking into account interval, probabilistic, and fuzzy uncertainty inherent in the corresponding decision making problems. The resulting application areas include environmental studies (selecting the best location for a meteorological tower), biology (selecting the most probable evolution history of a species), and engineering (designing the best control for a magnetic levitation train).

Content Level » Research

Keywords » Computational Intelligence - Constraint Programming - Decision Making - Decision Making Under Constraints

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity

Table of contents 

Algorithmics of Checking Whether a Mapping Is Injective, Surjective,
and/or Bijective.-

Simplicity Is Worse Than Theft: A Constraint-Based Explanation of a

Seemingly Counter-Intuitive Russian Saying.-

Continuous If-Then Statements Are Computable.-

Linear programming with Interval Type-2 fuzzy constraints.-

Epistemic Considerations on Expert Disagreement, Normative

Justification, and Inconsistency Regarding Multi-Criteria Decision Making .-Interval Linear Programming Techniques in Constraint Programming

and Global Optimization.-Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Interval Linear Programming Techniques in Constraint Programming

and Global Optimization.-Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

<.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Interval Linear Programming Techniques in Constraint Programming

and Global Optimization.-Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Gibbs Sampling as a Natural Statistical Analog of ConstraintsTechniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-<

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

<.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Interval Linear Programming Techniques in Constraint Programming

and Global Optimization.-Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of ConstraintsTechniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Gibbs Sampling as a Natural Statistical Analog of ConstraintsTechniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-<

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-<

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Interval Linear Programming Techniques in Constraint Programming

and Global Optimization.-Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

<.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Interval Linear Programming Techniques in Constraint Programming

and Global Optimization.-Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Interval Linear Programming Techniques in Constraint Programming

and Global Optimization.-Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168<

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Selecting the Best Location for a Meteorological Tower: A Case Study

of Multi-Objective Constraint Optimization.-Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Gibbs Sampling as a Natural Statistical Analog of Constraints

Techniques: Prediction in Science under General Probabilistic Uncertainty .-Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Why Tensors.-Adding Constraints – A (Seemingly Counterintuitive but) Useful

Heuristic in Solving Difficult Problems.-Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

Optimization of the Choquet Integral using Genetic Algorithm .-

Under Physics-Motivated Constraints, Generally-Non-Algorithmic Computational Problems Become Algorithmically Solvable.-

Constraint-Related Reinterpretation of Fundamental Physical

Equations Can Serve as a Built-In Regularization.-

Optimization of the Choquet Integral using Genetic Algorithm .-

Optimization of the Choquet Integral using Genetic Algorithm .-

Scalable, Portable, Verifiable Kronecker Products on Multi-Scale

Computers.-

Reliable and Robust Synthesis of QFT controller using ICSP.-

Towards an Efficient Bisection of Ellipsoids .-

.-

An Auto-validating Rejection Sampler for Differentiable Arithmetical

Expressions: Posterior Sampling of Phylogenetic Quartets.-

Graph Subdivision Methods in Interval Global Optimization .-

An Extended BDI-Based Model for Human Decision-Making and Social

Behavior: Various Applications .-

.-

Why Curvature in L-Curve: Combining Soft Constraints .-

.-

Surrogate Models for Mixed Discrete-Continuous Variables . . . . . . . . . . . . . 168

. . . . . . . . . . . . . 168

Laura P. Swiler, Patricia D. Hough, Peter Qian, Xu Xu, Curtis

Storlie, and Herbert Lee

Why Ellipsoid Constraints, Ellipsoid Clusters, and Riemannian

Space-Time: Dvoretzky’s Theorem Revisited. . . . . . . . . . . . . . . . . . . . . . . . . . 190

. . . . . . . . . . . . . . . . . . . . . . . . . . 190

Karen Villaverde, Olga Kosheleva, and Martine Ceberio

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