An interesting strategy called configuration checking (CC) was recently proposed to handle the cycling problem in local search for Minimum Vertex Cover. A natural question is whether this CC strategy also works for SAT. The direct application of CC did not result in stochastic local search (SLS) algorithms that can compete with the current best SLS algorithms for SAT. In this paper, we propose ...

Stochastic local search methods are widely used for solving positive instances of NP-hard problems. They are based on a partial exploration of a local search landscape characterized by a nite set, a tness function and a neighborhood relation. We think that theoretical or experimental study of these landscapes, independently of search algorithms , could provide a better understanding of local se...

Unsupervised kernel regression (UKR), the unsupervised counterpart of the Nadaraya-Watson estimator, is a dimension reduction technique for learning of low-dimensional manifolds. It is based on optimizing representative low-dimensional latent variables with regard to the data space reconstruction error. The problem of scaling initial local linear embedding solutions, and optimization in latent ...

A stochastic global optimization method based on a multistart strategy and a derivative-free filter local search for general constrained optimization is presented and analyzed. In the local search procedure, approximate descent directions for the constraint violation or the objective function are used to progress towards the optimal solution. The algorithm is able to locate all the local minima...

AutoRegressive Modular (ARM) processes are a new class of nonlinear stochastic processes, which can accurately model a large class of stochastic processes, by capturing the empirical distribution and autocorrelation function simultaneously. Given an empirical sample path, the ARM modeling procedure consists of two steps: a global search for locating the minima of a nonlinear objective function ...

the capacitated vehicle routing problem (cvrp) is a well-known combinatorial optimization problem that holds a central place in logistics management. the vehicle routing is an applied task in the industrial transportation for which an optimal solution will lead us to better services, save more time and ultimately increase in customer satisfaction. this problem is classified into np-hard problem...