This journal publishes research on the analysis and development of computational algorithms and modeling technology for optimization. It examines algorithms either for general classes of optimization problems or for more specific applied problems, stochastic algorithms as well as deterministic algorithms.
Computational Optimization and Applications covers a wide range of topics in optimization, including: large scale optimization, unconstrained optimization, constrained optimization, nondifferentiable optimization, combinatorial optimization, stochastic optimization, multiobjective optimization, and network optimization. It also covers linear programming, complexity theory, automatic differentiation, approximations and error analysis, parametric programming and sensitivity analysis, management science, and more.
This peer-reviewed journal features both research and tutorial papers that provide theoretical analysis, along with carefully designed computational experiments.
Officially cited as: Comput Optim Appl
- Publishes research on the analysis and development of computational algorithms and modeling technology for optimization.
- Examines algorithms either for general classes of optimization problems or for more specific applied problems, stochastic algorithms as well as deterministic algorithms.
- Covers a wide range of topics in optimization.
- William W. Hager
- Publishing model
- Hybrid. Learn about publishing OA with us
- 1.743 (2019)
- Impact factor
- 1.867 (2019)
- Five year impact factor
- 48 days
- Submission to first decision
- 390 days
- Submission to acceptance
- 89,727 (2019)
Authors (first, second and last of 4)
Momentum and stochastic momentum for stochastic gradient, Newton, proximal point and subspace descent methods
Accelerating convergence of the globalized Newton method to critical solutions of nonlinear equations
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About this journal
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