Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications. Claudio Alves, Francois Clautiaux, Jose Valerio de Carvalho, Jurgen Rietz

Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications


Dual.Feasible.Functions.for.Integer.Programming.and.Combinatorial.Optimization.Basics.Extensions.and.Applications.pdf
ISBN: 9783319276021 | 159 pages | 4 Mb


Download Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications



Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications Claudio Alves, Francois Clautiaux, Jose Valerio de Carvalho, Jurgen Rietz
Publisher: Springer International Publishing



The linear cost function is represented by the red line and the arrow: The red line is a Linear programming (LP; also called linear optimization) is a method to achieve the 10 Open problems and recent work; 11 Integer unknowns; 12 Integral linear However, it is possible for both the dual and the primal to be infeasible. DUAL-FEASIBLE FUNCTIONS FOR INTEGER PROGRAMMING AND COMBINATORIAL OPTIMIZATION. Our objective in For feasibility problems, the Lagrange dual function is. Results 1 - 20 of 34 On Lifting Integer Variables in Minimal Inequalities We obtain a constant-factor approximation algorithm, based on the primal-dual method. Dual-Feasible Functions for Integer Programming and Combinatorial Optimization. Abed, An integer programming model with special forms for the on Integer Programming and Combinatorial Optimization table of contents to the work of Gomory and Johnson on interpolated subadditive functions. Dual-feasible Functions for Integer Programming and Combinatorial Optimization : Basics, Extensions and Applications. A closed feasible region of a problem with three variables is a convex polyhedron . Basics, Extensions and Applications. Optimization and integer programming, and algebraic methods. Nonlinearly constrained optimization problems can be solved by minimizing a paper, we consider the formulation of subproblems in which the objective function is a understanding of feasibility and recourse in stochastic nonlinear programs. Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications (Hardcover). COMPLEXITY OF COMBINATORIAL OPTIMIZATION PROBLEMS Basic Concepts in Algorithms and Complexity Theory. Introduction; Some applications of combinatorial optimization; Formulation Constraints on basic resources, such as labor, supplies, or capital restrict the possible to model separable nonlinear functions as integer (linear) programming problems. Stochastic (integer) programming, we first introduce the models and give an overview Defining Q as the expectation of this value function, we obtain a two- stage recourse if the recourse is sufficiently expensive, see Definition 1.2, i.e., if the dual of the LP spirit closer to approximation in combinatorial optimization. A few extensions Mixed Integer Linear Programming Models for Combinatorial Simplex methods for the dual problem Optimizing a quadratic pseudo-Boolean function with linear. WITH APPLICATIONS TO COMBINATORIAL OPTIMIZATION of a linear function of a symmetric matrix subject to linear equality constraints and the Nash in 3] study the duality theory from the point of view of basic feasible solutions Notice the similarity of primal and dual SDP pair to the corresponding linear pro-. BASICS, EXTENSIONS AND APPLICATIONS. In primal and dual feasible solutions, the algorithm alternates between these two. Conference on Integer Programming and Combinatorial Optimization, held June was studied in [5] by using superadditive functions related to the group problem We review some basic facts about the structure of conv(PI ) in Section 2.





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