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Approximate dynamic programming: solving the curses of dimensionality
  • P. Pardalos
  • Mathematics, Computer Science
  • Optim. Methods Softw.
  • 1 February 2009
This book provides detailed coverage of modelling decision processes under uncertainty, robustness, designing and estimating value function approximations, choosing effective step-size rules, and convergence issues and is an excellent textbook for advanced undergraduate and beginning graduate students. Expand
Quadratic Assignment Problem
The quadratic assignment problem (QAP) was introduced by Koopmans and Beckmann in 1957 as a mathematical model for the location of a set of indivisible economical activities and can be formulated as follows. Expand
Handbook of global optimization
Preface. 1. Tight relaxations for nonconvex optimization problems using the Reformulation-Linearization/Convexification Technique (RLT) H.D. Sherali. 2. Exact algorithms for global optimization ofExpand
The maximum clique problem
A survey of results concerning algorithms, complexity, and applications of the maximum clique problem is presented and enumerative and exact algorithms, heuristics, and a variety of other proposed methods are discussed. Expand
An exact algorithm for the maximum clique problem
A partially enumerative algorithm is presented for the maximum clique problem which is very simple to implement. Computational results for an efficient implementation on an IBM 3090 computer areExpand
Introduction to Global Optimization
Detecting critical nodes in sparse graphs
This paper focuses on detecting critical nodes, or nodes whose deletion results in the minimum pair-wise connectivity among the remaining nodes, and proposes a heuristic for the problem which exploits the combinatorial structure of the graph. Expand
A Collection of Test Problems for Constrained Global Optimization Algorithms
Quadratic programming test problems.- Quadratically constrained test problems.- Nonlinear programming test problems.- Distillation column sequencing test problems.- Pooling/blending test problems.-Expand
Quadratic programming with one negative eigenvalue is NP-hard
We show that the problem of minimizing a concave quadratic function with one concave direction is NP-hard. This result can be interpreted as an attempt to understand exactly what makes nonconvexExpand
Constrained Global Optimization: Algorithms and Applications
This paper presents a meta-modelling framework for solving the optimization problems that can be formulated as nonconvex quadratic problems and some of the methods used for solving these problems have been developed. Expand