Discrete-event simulation based optimization is the process of finding the optimum design of a stochastic system when the performance measure(s) could only be estimated via simulation. Randomness in simulation outputs often challenges the correct selection of the optimum. We propose an algorithm that merges Ranking and Selection procedures with a large class of random search methods for continu...

We compare an efficiency of a deterministic " lawnmower " and random search strategies for finding a prescribed sequence of letters (a password) of length M in which all letters are taken from the same Q-ary alphabet. We show that at best a random search takes two times longer than a " lawnmower " search.

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The paper presents several results that address a fundamental question in low-rank matrices recovery: how many measurements are needed to recover low rank matrices? We begin by investigating the complex matrices case and show that 4nr−4r generic measurements are both necessary and sufficient for the recovery of rank-r matrices in C by algebraic tools developed in [10]. Thus, we confirm a conjec...

We investigate the beneet of Tabu Search for satissability (SAT) and constraint satisfaction problems (CSP) and compare it to the more frequently used random walk heuristic. We argue, that a more deterministic direction of search as done with Tabu Search is worth considering also for SAT and CSP. We give experimental evidence that Tabu Search can be used to eeciently guide local search procedur...

The typical difficulty of various NP-hard problems varies with simple parameters describing their structure. This behavior is largely independent of the search algorithm, but depends on the choice of problem ensemble. A given problem instance belongs to many different ensembles, so applying these observations to individual problems requires identifying which ensemble is most appropriate for pre...