CitationAlexander H. Gose Graduate Program of Operations Research, North Carolina State University, Raleigh, NC 27695, ahgose@ncsu.edu Brian T. Denton Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, btdenton@umich.edu October 17, 2014 (Accepted for publication to INFORMS Journal on Computing)
In this paper, we present a new conditional gradient type method for convex optimization by utilizing a linear optimization (LO) oracle to minimize a series of linear functions over the feasible set. Different from the classic conditional gradient method, the conditional gradient sliding (CGS) algorithm developed herein can skip the computation of gradients from time … Read more
We study the robust constrained shortest path problem under resource uncertainty. After proving that the problem is \NPhard in the strong sense for arbitrary uncertainty sets, we focus on budgeted uncertainty sets introduced by Bertsimas and Sim (2003) and their extension to variable uncertainty by Poss (2013). We apply classical techniques to show that the … Read more
Due to strict regulatory rules in combination with complex nonlinear physics, major gas network operators in Germany and Europe face hard planning problems that call for optimization. In part 1 of this paper we have developed a suitable model hierarchy for that purpose. Here we consider the more practical aspects of modeling. We validate individual … Read more
In this paper we study proximal conditional-gradient (CG) and proximal gradient-projection type algorithms for a block-structured constrained nonconvex optimization model, which arises naturally from tensor data analysis. First, we introduce a new notion of $\epsilon$-stationarity, which is suitable for the structured problem under consideration. %, compared with other similar solution concepts. We then propose two … Read more
We address combinatorial problems that can be formulated as minimization of a partially separable function of discrete variables (energy minimization in graphical models, weighted constraint satisfaction, pseudo-Boolean optimization, 0-1 polynomial programming). For polyhedral relaxations of such problems it is generally not true that variables integer in the relaxed solution will retain the same values in … Read more
A class of trust-region algorithms is developed and analyzed for the solution of optimization problems with nonlinear equality and inequality constraints. Based on composite-step trust region methods and a filter approach, the resulting algorithm also does not require the computation of exact Jacobians; only Jacobian vector products are used along with approximate Jacobian matrices. As … Read more
The recent research on the CVRP is being slowed down by the lack of a good set of benchmark instances. The existing sets suff er from at least one of the following drawbacks: (i) became too easy for current algorithms; (ii) are too arti cial; (iii) are too homogeneous, not covering the wide range of characteristics found … Read more
A traffic matrix $D_1$ dominates a traffic matrix $D_2$ if any capacity reservation supporting $D_1$ supports $D_2$ as well. We prove that $D_3$ dominates $D_3+ \lambda(D_2-D_1)$ for any $\lambda\geq 0$ if $D_1$ dominates $D_2$. By the property , it is pointed out that the domains supported by different traffic matrices are isomorphic on the extended … Read more
We consider the Robust Network Loading problem with splittable flows and demands that belong to the budgeted uncertainty set. We compare the optimal solution cost and computational cost of the problem when using static routing, volume routing, affine routing, and dynamic routing. For the first three routing types, we compare the compact formulation with a … Read more