Bøger i MOS-SIAM Series on Optimization serien

The absence of derivatives, often combined with the presence of noise or lack of smoothness, is a major challenge for optimization. This book explains how sampling and model techniques are used in derivative-free methods and how these methods are designed to efficiently and rigorously solve optimization problems.

Examines well-structured and thus efficiently solvable convex optimization problems, with emphasis on conic quadratic and semidefinite programming. The authors present the basic theory underlying these problems as well as their numerous applications in engineering, including synthesis of filters, Lyapunov stability analysis, and structural design.

A self-contained introduction to linear programming using MATLAB(R) software.

Addresses modern nonlinear programming (NLP) concepts and algorithms, especially as they apply to challenging applications in chemical process engineering. The author provides a firm grounding in fundamental NLP properties and algorithms, and relates them to real-world problem classes in process optimization.

Provides a self-contained, accessible introduction to the mathematical advances and challenges resulting from the use of semidefinite programming in polynomial optimization. This quickly evolving research area with contributions from the diverse fields of convex geometry, algebraic geometry, and optimization is known as convex algebraic geometry.

This primarily undergraduate textbook focuses on finite-dimensional optimization. It offers an original and well integrated treatment of semidifferential calculus and optimization, with an emphasis on the Hadamard subdifferential, introduced at the beginning of the 20th century and somewhat overlooked for many years

This collection presents recent results in the areas of theoretical and computational sides of integer programming and combinatorial optimization.

This is the first book devoted to the full scale of applications of stochastic programming and also the first to provide access to publicly available algorithmic systems. It introduces the power of stochastic programming to a wider audience and demonstrates the application areas where this approach is superior to other modeling approaches.

Takes the reader who knows little of interior-point methods to within sight of the research frontier.

Contains a thorough overview of the rapidly growing field of global optimization, with chapters on key topics such as complexity, heuristic methods, derivation of lower bounds for minimization problems, and branch-and-bound methods and convergence.

A comprehensive treatment of optimization problems involving uncertain parameters for which stochastic models are available.