The memory alignment of executable code can have significant yet hard to predict effects on the run-time performance of programs, even in the presence of existing aggressive optimization. We present an investigation of two different approaches to instruction cache optimization—the first is an implementation of a well-known and established compile-time heuristic, and the second is our own stocha...

Owning to similar business nature, it should be possible to directly migrate successful airline revenue management techniques to the hotel domain. However, one of the salient di0erences between airlines and hotels is rarely highlighted—the network structure of length of stay or the displacement e0ect. The hotel patrons go from a 4rst stay-over night to a last stay-over night in consecutive nigh...

“Stochastic simulation optimization” (often shortened as simulation optimization) refers to stochastic optimization using simulation. Specifically, the underlying problem is stochastic and the goal is to find the values of controllable parameters (decision variables) to optimize some performance measures of interest, which are evaluated via stochastic simulation, such as discrete-event simulati...

We present an optimization approach for linear SVMs based on a stochasticprimal-dual approach, where the primal step is akin to an importance-weightedSGD, and the dual step is a stochastic update on the importance weights. Thisyields an optimization method with a sublinear dependence on the training setsize, and the first method for learning linear SVMs with runtime less the...

In this paper, we focus in developing a stochastic model for topology optimization. The principal objective of such a model is to find robust structures for a given main load having a stochastic behavior. In the first part, we present the expected compliance formulation and some results in topology optimization. Then, in order to illustrate the interest of our approach, we consider a preliminar...

In recent years, much attention has been devoted to the development of metaheuristics and local search algorithms for tackling stochastic combinatorial optimization problems. In this paper, we propose an effective local search algorithm that makes use of empirical estimation techniques for a class of stochastic combinatorial optimization problems. We illustrate our approach and assess its perfo...

In this paper, we are interested in the development of efficient algorithms for convex optimization problems in the simultaneous presence of multiple objectives and stochasticity in the first-order information. We cast the stochastic multiple objective optimization problem into a constrained optimization problem by choosing one function as the objective and try to bound other objectives by appr...