Алгебраїчний підхід до реоптимізації задач комбінаторної оптимізації та суміжні питання оцінки складності обчислень

Використовується поняття αΛ -наближеного поліморфізму для конструювання ψ(αΛ)-наближеного оптимального алгоритму (ψ(αΛ) = 2-1/αΛ) для реоптимізації CSP задачі MAX - Λ ( Ins - MAX - Λ) з добавленням деякого обмеження. Гіпотеза алгебраїчної дихотомії характеризує NP-складність розглянутого підходу, а базова SDP релаксація для наближених поліморфізмів (BasicSDP) визначає ефективний алгоритм заокруглення для MAX - Λ та Ins - MAX - Λ.The concept of αΛ -approximation polymorphism is used for design of ψ(αΛ)-approximation optimal algorithm (ψ(αΛ) = 2-1/αΛ) for reoptimization of CSP problem MAX - Λ ( Ins - MAX - Λ) with addition of some constraint. Algebraic dichotomy conjecture characterizes NP - hardness of the considered approach and basic SDP relaxation for approximation polymorphism ( BasicSDP ) defines an efficient rounding algorithm for MAX - Λ and Ins - MAX - Λ

Robust Reoptimization of Steiner Trees

In reoptimization, one is given an optimal solution to a problem instance and a (locally) modified instance. The goal is to obtain a solution for the modified instance. We aim to use information obtained from the given solution in order to obtain a better solution for the new instance than we are able to compute from scratch. In this paper, we consider Steiner tree reoptimization and address the optimality requirement of the provided solution. Instead of assuming that we are provided an optimal solution, we relax the assumption to the more realistic scenario where we are given an approximate solution with an upper bound on its performance guarantee. We show that for Steiner tree reoptimization there is a clear separation between local modifications where optimality is crucial for obtaining improved approximations and those instances where approximate solutions are acceptable starting points. For some of the local modifications that have been considered in previous research, we show that for every fixed ε>0, approximating the reoptimization problem with respect to a given (1+ε)-approximation is as hard as approximating the Steiner tree problem itself. In contrast, with a given optimal solution to the original problem it is known that one can obtain considerably improved results. Furthermore, we provide a new algorithmic technique that, with some further insights, allows us to obtain improved performance guarantees for Steiner tree reoptimization with respect to all remaining local modifications that have been considered in the literature: a required node of degree more than one becomes a Steiner node; a Steiner node becomes a required node; the cost of one edge is increased

A survey on combinatorial optimization in dynamic environments

This survey presents major results and issues related to the study of NPO problems in dynamic environments, that is, in settings where instances are allowed to undergo some modifications over time. In particular, the survey focuses on two complementary frameworks. The first one is the reoptimization framework, where an instance I that is already solved undergoes some local perturbation. The goal is then to make use of the information provided by the initial solution to compute a new solution. The second framework is probabilistic optimization, where the instance to optimize is not fully known at the time when a solution is to be proposed, but results from a determined Bernoulli process. Then, the goal is to compute a solution with optimal expected value