solutions to this problem. Corporate finance has relied on clustered standard errors, while asset pricing has used the Fama-MacBeth procedure to estimate standard errors. This paper examines the different methods used in the literature and explains when the different methods yield the same (and correct

for some ρ> 0. In short, f can be recovered exactly by solving a simple convex optimization problem (which one can recast as a linear program). In addition, numerical experiments suggest that this recovery procedure works unreasonably well; f is recovered exactly even in situations where a significant

of scientific interest accurately and sometimes even exactly from a number of samples which is far smaller than the desired resolution of the image/signal, e.g. the number of pixels in the image. It is believed that compressive sampling has far reaching implications. For example, it suggests the possibility

characteristics of the procedure are presented. Moreover, we compare the proposed scatter search algorithm with an exact branch-and-price procedure and a steepest descent variable neighborhood search.

for the prob-lem, a branch-and-price algorithm, and heuristic variants of this algorithm are described. Computational experiments with the algorithms are pre-sented and discussed. The experiments are performed on randomly gener-ated instances structured to resemble real-world situations and on TTRP benchmark

of an infected patient, are taken into account. A branch-and-price methodology will be introduced in order to develop both exact and heuristic algorithms. In this methodology, column generation is used to optimize the linear programming formu-lation of the scheduling problem. Both a dynamic programming approach

Abstract: This paper presents a branch-and-price algorithm for scheduling n jobs on m nonhomogeneous parallel machines with multiple time windows. An additional feature of the problem is that each job falls into one of ρ priority classes and may require two operations. The objective is to maximize

The stochastic simulation algorithm ͑SSA͒ is an essentially exact procedure for numerically simulating the time evolution of a well-stirred chemically reacting system. Despite recent major improvements in the efficiency of the SSA, its drawback remains the great amount of computer time

We describe an exact algorithm for the problem of sorting a permutation by the minimum number of reversals, originating from evolutionary studies in molecular biology. Our approach is based on an integer linear programming formulation of a graph-theoretic relaxation of the problem, calling for a

for the problem, which is based on column generation and branch-and-price. Computational results on a large set of instances from the literature are reported, showing excellent performance when compared with the best heuristic algorithms from the literature. 1