Publications

Hart, William E.; Berry, Jonathan W.; Watson, Jean-Paul W.; Carr, Robert D.; Phillips, Cynthia A.; Leung, Vitus J.

Abstract not provided.

Carr, Robert D.; Berry, Jonathan W.; Hart, William E.; Phillips, Cynthia A.; Watson, Jean-Paul W.

Abstract not provided.

Mathematical Programming

Carr, Robert D.; Greenberg, Harvey J.; Hart, William E.; Konjevod, Goran; Lauer, Erik; Lin, Henry; Morrison, Tod; Phillips, Cynthia A.

We present a series of related robust optimization models for placing sensors in municipal water networks to detect contaminants that are maliciously or accidentally injected. We formulate sensor placement problems as mixed-integer programs, for which the objective coefficients are not known with certainty. We consider a restricted absolute robustness criteria that is motivated by natural restrictions on the uncertain data, and we define three robust optimization models that differ in how the coefficients in the objective vary. Under one set of assumptions there exists a sensor placement that is optimal for all admissible realizations of the coefficients. Under other assumptions, we can apply sorting to solve each worst-case realization efficiently, or we can apply duality to integrate the worst-case outcome and have one integer program. The most difficult case is where the objective parameters are bilinear, and we prove its complexity is NP-hard even under simplifying assumptions. We consider a relaxation that provides an approximation, giving an overall guarantee of near-optimality when used with branch-and-bound search. We present preliminary computational experiments that illustrate the computational complexity of solving these robust formulations on sensor placement applications.

Journal of the Operation Research Letters

Carr, Robert D.

Abstract not provided.

Proposed for publication in the Journal of the Discrete Optimization.

Carr, Robert D.

While it had been known for a long time how to transform an asymmetric traveling salesman (ATS) problem on the complete graph with n vertices into a symmetric traveling salesman (STS) problem on an incomplete graph with 2n vertices, no method was available for using this correspondence to derive facets of the symmetric polytope from facets of the asymmetric polytope until the work of E. Balas and M. Fischetti in [Lifted cycle inequalities for the asymmetric traveling salesman problem, Mathematics of Operations Research 24 (2) (1999) 273-292] suggested an approach. The original Balas-Fischetti method uses a standard sequential lifting procedure for the computation of the coefficient of the edges that are missing in the incomplete STS graph, which is a difficult task when addressing classes of (as opposed to single) inequalities. In this paper we introduce a systematic procedure for accomplishing the lifting task. The procedure exploits the structure of the tight STS tours and organizes them into a suitable tree structure. The potential of the method is illustrated by deriving large new classes of facet-defining STS inequalities.

Lauer, Erik; Carr, Robert D.

Abstract not provided.

4OR

Carr, Robert D.; Lancia, Giuseppe G.

In Combinatorial Optimization, one is frequently faced with linear programming (LP) problems with exponentially many constraints, which can be solved either using separation or what we call compact optimization. The former technique relies on a separation algorithm, which, given a fractional solution, tries to produce a violated valid inequality. Compact optimization relies on describing the feasible region of the LP by a polynomial number of constraints, in a higher dimensional space. A commonly held belief is that compact optimization does not perform as well as separation in practice. In this paper,we report on an application in which compact optimization does in fact largely outperform separation. The problem arises in structural proteomics, and concerns the comparison of 3-dimensional protein folds. Our computational results show that compact optimization achieves an improvement of up to two orders of magnitude over separation. We discuss some reasons why compact optimization works in this case but not, e.g., for the LP relaxation of the TSP. © Springer-Verlag 2004.

Proposed for publication in INFORMS J on Computing.

Hart, William E.; Hart, William E.; Carr, Robert D.

The maximum contact map overlap (MAX-CMO) between a pair of protein structures can be used as a measure of protein similarity. It is a purely topological measure and does not depend on the sequence of the pairs involved in the comparison. More importantly, the MAX-CMO present a very favorable mathematical structure which allows the formulation of integer, linear and Lagrangian models that can be used to obtain guarantees of optimality. It is not the intention of this paper to discuss the mathematical properties of MAX-CMO in detail as this has been dealt elsewhere. In this paper we compare three algorithms that can be used to obtain maximum contact map overlaps between protein structures. We will point to the weaknesses and strengths of each one. It is our hope that this paper will encourage researchers to develop new and improve methods for protein comparison based on MAX-CMO.

Proposed for publication in Mathematical Programming.

Carr, Robert D.

Abstract not provided.

Proposed for publication in Journal of Math Programming.

Carr, Robert D.; Carr, Robert D.

Abstract not provided.

Proposed for publication in the Journal of 40R.

Carr, Robert D.; Carr, Robert D.

Abstract not provided.

Operations Research Letters

Carr, Robert D.; Lancia, Giuseppe

In this paper, we illustrate by means of examples a technique for formulating compact (i.e. polynomial-size) linear programming relaxations in place of exponential-size models requiring separation algorithms. In the same vein as a celebrated theorem by Grötschel, Lovász and Schrijver, we state the equivalence of compact separation and compact optimization. Among the examples used to illustrate our technique, we introduce a new formulation for the traveling salesman problem, whose relaxation we show as an equivalent to the subtour elimination relaxation. © 2001 Elsevier Science B.V. All rights reserved.

Carr, Robert D.; Istrail, Sorin I.

Abstract not provided.

Carr, Robert D.

The authors present a new technique for the design of approximation algorithms that can be viewed as a generalization of randomized rounding. They derive new or improved approximation guarantees for a class of generalized congestion problems such as multicast congestion, multiple TSP etc. Their main mathematical tool is a structural decomposition theorem related to the integrality gap of a relaxation.