We introduce the notion of a stable instance for a discrete optimization problem, and argue that in many practical situations only sufficiently stable instances are of interest. The question then arises whether stable instances of NP–hard problems are easier to solve. In particular, whether there exist algorithms that solve correctly and in polynomial time all sufficiently stable instances of s...

In this paper we aim at proposing a reinforcement learning based model for solving combinatorial optimization problems. Combinatorial optimization problems are hard to solve optimally, that is why any attempt to improve their solutions is beneficent. We are particularly focusing on the bidimensional protein folding problem, a well known NP-hard optimizaton problem important within many fields i...

Many problems in signal processing, machine learning and computer vision can be solved by learning low rank models from data. In computer vision, problems such as rigid structure from motion have been formulated as an optimization over subspaces with fixed rank. These hard -rank constraints have traditionally been imposed by a factorization that parameterizes subspaces as a product of two matri...

the multiple traveling salesman problem (mtsp) is a generalization of the famous traveling salesman problem (tsp), where more than one salesman is used in the solution. although the mtsp is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing problems. this paper presents an efficient and evolutionary optimization algorith...

A tournament is an oriented complete graph. The feedback arc set problem for tournaments is the optimization problem of determining the minimum possible number of edges of a given input tournament T whose reversal makes T acyclic. Ailon, Charikar and Newman showed that this problem is NP-hard under randomized reductions. Here we show that it is in fact NP-hard. This settles a conjecture of Bang...

We introduce the notion of a stable instance for a discrete optimization problem, and argue that in many practical situations only sufficiently stable instances are of interest. The question then arises whether stable instances of NP–hard problems are easier to solve. In particular, whether there exist algorithms that solve correctly and in polynomial time all sufficiently stable instances of s...

We present a natural fitness function f for the multiobjective shortest path problem, which is a fundamental multiobjective combinatorial optimization problem known to be NP-hard. Thereafter, we conduct a rigorous runtime analysis of a simple evolutionary algorithm (EA) optimizing f . Interestingly, this simple general algorithm is a fully polynomial-time randomized approximation scheme (FPRAS)...

The QoS-driven Service Selection (QSS) problem is a wellknown NP-hard problem in the combinatorial optimization field. Although the QSS problem is naturally multi-objective optimization problem, most of the existing approaches solve the problem in single-objective optimization context. In the recent years, there have been some efforts to tackle the problem in multi-objective optimization contex...

the aim of this paper is to study a multi-product, multi-period production systems in a hybrid flow shop so that lot-sizing and scheduling will be detemined simultaneously. a new mixed-integer programming model is proposed to formulate the studied problem. the objective function in this investigation includes the total cost of production, inventory and external supply. in the case of not satisf...

This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the δ-cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard to implement in practice. Also, the existing Bayesian optimization method with exp...