Interdicting Structured Combinatorial Optimization Problems with {0, 1}-Objectives

Interdicting Structured Combinatorial Optimization Problems with {0, 1}-Objectives

Interdiction problems ask about the worst-case impact of a limited change to an underlying optimization problem. They are a natural way to measure the robustness of a system, or to identify its weakest spots. Interdiction problems have been studied for a wide variety of classical combinatorial optimization problems, including maximum s-t flows, shortest s-t paths, maximum weight matchings, mini...

0 . 1 Combinatorial Problems of ( Quasi - ) Crystallography

Potential reduction algorithms for structured combinatorial optimization problems

Recently Karmarkar proposed a potential reduction algorithm for binary feasibility problems. In this paper we point out a practical drawback of his potential function and we propose a modi ed potential function that is computationally more attractive. As the main result of the paper, we will consider a special class of binary feasibility problems, and show how problems of this class can be refo...

Optimization Problems with Value Function Objectives

The family of optimization problems with value function objectives includes the minmax programming problem and the bilevel optimization problem. In this paper, we derive necessary optimality conditions for this class of problems. The main focus is on the case where the functions involved are nonsmooth and the constraints are the very general operator constraints.

Combinatorial Optimization Problems with Testing

In stochastic versions of combinatorial optimization problems, the goal is to optimize an objective function under constraints that specify the feasible solutions, when some parameters involve uncertainty. We focus on maximizing a linear objective ∑N i=1Wixi, where x1, . . . , xN are the decision variables, W1, . . . ,WN are mutually independent random coefficients, and the constraints are dete...