Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Control and Cybernetics

2 Libura M.

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: accuracy function

Sortuj według:

Ogranicz wyniki do:

On the robustness of optimal solutions for combinatorial optimization problems

Libura M.

Control and Cybernetics

2009

Vol. 38, no 3

671-685

We consider the so-called generic combinatorial optimization problem, where the set of feasible solutions is some family of subsets of a finite ground set with specified positive initial weights of elements, and the objective function represents the total weight of elements of a feasible solution. We assume that the weights of all elements may be perturbed simultaneously and independently up to a given percentage of their initial values. A feasible solution which minimizes the worst-case relative regret, is called a robust solution. The maximum percentage level of perturbations, for which an initially optimal solution remains robust, is called the robustness radius of this solution. In this paper we study the robustness aspect of initially optimal solutions and provide lower bounds for their robustness radii.

Quality of solutions for perturbed combinatorial optimization problems

Libura M.

Control and Cybernetics

2000

Vol. 29, no 1

199-219

We consider a general combinatorial optimization problem in which the set of feasible solutions is defined as a given and fixed family of subsets for some finite ground set. To any element of the ground set the so-called weight is associated. The problem consists in finding a feasible subset for which the sum of weights of its elements is the minimum. When the weights of elements vary or are estimated with some accuracy, then the solution of the problem obtained for some initial weights may appear non-optimal. In this paper we consider the quality of a given solution in the case of weights perturbation or inaccuracy. Namely, we study the relative error of a given solution as a function of particular weights perturbation. We also calculate the maximum perturbation or estimation errors of weights which preserve the optimality of a given solution of the problem.