Optimization

1. Front Matter

   Pages i-xx

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2. Unconstrained Optimization

   • Elijah Polak

   Pages 1-166

3. Finite Min-Max and Constrained Optimization

   • Elijah Polak

   Pages 167-367

4. Semi-Infinite Optimization

   • Elijah Polak

   Pages 368-481

5. Optimal Control

   • Elijah Polak

   Pages 482-645

6. Mathematical Background

   • Elijah Polak

   Pages 646-742

7. Back Matter

   Pages 743-782

   PDF

This book deals with optimality conditions, algorithms, and discretization tech­ niques for nonlinear programming, semi-infinite optimization, and optimal con­ trol problems. The unifying thread in the presentation consists of an abstract theory, within which optimality conditions are expressed in the form of zeros of optimality junctions, algorithms are characterized by point-to-set iteration maps, and all the numerical approximations required in the solution of semi-infinite optimization and optimal control problems are treated within the context of con­ sistent approximations and algorithm implementation techniques. Traditionally, necessary optimality conditions for optimization problems are presented in Lagrange, F. John, or Karush-Kuhn-Tucker multiplier forms, with gradients used for smooth problems and subgradients for nonsmooth prob­ lems. We present these classical optimality conditions and show that they are satisfied at a point if and only if this point is a zero of an upper semicontinuous optimality junction. The use of optimality functions has several advantages. First, optimality functions can be used in an abstract study of optimization algo­ rithms. Second, many optimization algorithms can be shown to use search directions that are obtained in evaluating optimality functions, thus establishing a clear relationship between optimality conditions and algorithms. Third, estab­ lishing optimality conditions for highly complex problems, such as optimal con­ trol problems with control and trajectory constraints, is much easier in terms of optimality functions than in the classical manner. In addition, the relationship between optimality conditions for finite-dimensional problems and semi-infinite optimization and optimal control problems becomestransparent.

• Newton's method
• Optimal control
• Optimality Conditions
• Optimization algorithm
• Optimization algorithms
• Quasi-Newton method
• algorithm
• algorithms
• linear optimization
• nonlinear optimization
• optimization
• programming

• Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA

  Elijah Polak

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