Dual row modules and polyhedra of blocking group problems

Corresponding to every group problem is a row module. Duality for group problems is developed using the duality or orthogonality of the corresponding row modules. The row module corresponding to a group problem is shown to include Gomory's fractional cuts for the group polyhedron and all the vertices of the polyhedron of the blocking group problem. The polyhedra corresponding to a pair of blocking group problems are shown to have a blocking nature i.e. the vertices of one include some of the facets of the other and mutatis mutandis. The entire development is constructive. The notions of contraction, deletion, expansion and extension are defined constructively and related to homomorphic liftings and suproblems in a dual setting. Roughly speaking a homomorphic lifting is dual to forming a subproblem. A proof of the Gastou-Johnson generalization of Gomory's homomorphic lifting theorem is given, and dual constructions are discussed. A generalization of Gomory's subadditive characterization to subproblems is given. In the binary case, it is closely related to the work of Seymour on cones arising from binary matroids.     

A combinatorial formula for affine Hall–Littlewood functions via a weighted Brion theorem

We present a new combinatorial formula for Hall–Littlewood functions associated with the affine root system of type \({{\tilde{A}}}_{n-1}\), i.e., corresponding to the affine Lie algebra \({{\widehat{\mathfrak {sl}}}}_n\). Our formula has the form of a sum over the elements of a basis constructed by Feigin, Jimbo, Loktev, Miwa and Mukhin in the corresponding irreducible representation. Our formula can be viewed as a weighted sum of exponentials of integer points in a certain infinite-dimensional convex polyhedron. We derive a weighted version of Brion’s theorem and then apply it to our polyhedron to prove the formula.     

Stable sets, corner polyhedra and the Chvátal closure

We consider the edge formulation of the stable set problem. We characterize its corner polyhedron, i.e. the convex hull of the points satisfying all the constraints except the non-negativity of the basic variables. We show that the non-trivial inequalities necessary to describe this polyhedron can be derived from one row of the simplex tableau as fractional Gomory cuts. It follows that the split closure is not stronger than the Chvátal closure for the edge relaxation of the stable set problem.     

Parallel Algorithms for Solving a Class of Variational Inequalities over the Common Fixed Points Set of a Finite Family of Demicontractive Mappings

Abstract

We propose parallel algorithms for solving a class of variational inequalities over the set of common fixed points for a finite family of demicontractive mappings in real Hilbert spaces. Under some suitable conditions, we prove that the sequence generated by the proposed algorithms converges strongly to a solution of the problem. We apply the proposed algorithms to strongly monotone variational inequality problems with pseudomonotone equilibrium constraints by defining a quasi-nonexpansive and demi-closed mapping whose fixed point set coincides with the solution set of the equilibrium problem.     

Enumerative technique for an extreme point fractional program

This paper presents a technique for solving a linear fractional functionals program whose optimum is supposed to occur at one of the extreme points of a convex polyhedron. This polyhedron is defined by a system of linear inequalities with non-negative restrictions on the variables. The approach is a dual method type in the sense that feasibility is achieved only at the optimal solution.     

Upper bounding in inner regions for global optimization under inequality constraints

In deterministic continuous constrained global optimization, upper bounding the objective function generally resorts to local minimization at several nodes/iterations of the branch and bound. We propose in this paper an alternative approach when the constraints are inequalities and the feasible space has a non-null volume. First, we extract an inner region, i.e., an entirely feasible convex polyhedron or box in which all points satisfy the constraints. Second, we select a point inside the extracted inner region and update the upper bound with its cost. We describe in this paper two original inner region extraction algorithms implemented in our interval B&B called IbexOpt (AAAI, pp 99–104, 2011). They apply to nonconvex constraints involving mathematical operators like , \( +\; \bullet ,\; /,\; power,\; sqrt,\; exp,\; log,\; sin\) . This upper bounding shows very good performance obtained on medium-sized systems proposed in the COCONUT suite.     

Efficient enumeration of the vertices of polyhedra associated with network LP's

Algorithms are given to list the vertices of polyhedra associated with network linear programs and their duals. Each algorithm has running time which is quadratic in the number of vertices of the polyhedron, and does not require that the polyhedron be either bounded or simple. The algorithms use characterizations of adjacent vertices in network and dual network LP's to perform an efficient traversal of the edge graph of the polyhedron. This contrasts with algorithms for enumerating the vertices of a general polyhedron, all of which have worst-case complexity which is exponential in the number of vertices of the polyhedron.     

Surrogate-RLT cuts for zero–one integer programs

In this paper, we consider the class of 0–1 integer problems and develop an effective cutting plane algorithm that gives valid inequalities called surrogate-RLT cuts (SR cuts). Here we implement the surrogate constraint analysis along with the reformulation–linearization technique (RLT) to generate strong valid inequalities. In this approach, we construct a tighter linear relaxation by augmenting SR cuts to the problem. The level-\(d\) SR closure of a 0–1 integer program is the polyhedron obtained by intersecting all the SR cuts obtained from RLT polyhedron formed over each set of \(d\) variables with its initial formulation. We present an algorithm for approximately optimizing over the SR closure. Finally, we present the computational result of SR cuts for solving 0–1 integer programming problems of well-known benchmark instances from MIPLIB 3.0.     

The Chvátal closure of generalized stable sets in bidirected graphs

We consider the set of integral solutions of $Ax⩽b$, $x⩾0$, where A is the edge-vertex incidence matrix of a bidirected graph. We characterize its corner polyhedron, i.e. the convex hull of the points satisfying all the constraints except the non-negativity of the basic variables. We show that the non-trivial inequalities necessary to describe this polyhedron can be derived as fractional Gomory cuts. It follows in particular that the split closure is equal to the Chvátal closure in this case.     

Signature classes of transportation polytopes

Signature algorithms solve certain classes of transportation problems in a number of steps bounded by the diameter of the dual polyhedron. The class of problems to which signature algorithms may be applied—the signature classes of the title—are characterized, and the monotonic Hirsch conjecture is shown to hold for them. In addition, certain more precise results are given for different cases. Finally, it is explained why a supposed proof of the Hirsch conjecture for all transportation polytopes is incomplete and apparently irremedial.Dedicated with affection to Philip Wolfe on the occasion of his 65th birthday.     

The Representation Polyhedron of a Semiorder

Let a finite semiorder, or unit interval order, be given. When suitably defined, its numerical representations are the solutions of a system of linear inequalities. They thus form a convex polyhedron. We show that the facets of the representation polyhedron correspond to the noses and hollows of the semiorder. Our main result is to prove that the system defining the polyhedron is totally dual integral. As a consequence, the coordinates of the vertices and the components of the extreme rays of the polyhedron are all integral multiples of a common value. Total dual integrality is in turn derived from a particular property of the oriented cycles in the directed graph of noses and hollows of a strictly upper diagonal step tableau. Our approach delivers also a new proof for the existence of the minimal representation of a semiorder, a concept originally discovered by Pirlot (Theory Decis 28:109–141, 1990). Finding combinatorial interpretations of the vertices and extreme rays of the representation polyhedron is left for future work.     

On the dominating set polytope of web graphs

The dominating set polyhedron of a web graph $W_n^k$ is the set covering polyhedron of a circulant matrix $C_n^{2k+1}$. Working on the set covering polyhedron of general circulant matrices, Argiroffo and Bianchi found a class of valid inequalities, induced by a particular family of circulant minors. The authors also identified sufficient conditions on the minors in order to induce facet defining inequalities. In this work we generalize these results for new valid inequalities associated with every circulant minor. We conjecture that, for any k, the minor inequalities together with the boolean facets are enough to describe the set covering polyhedron of $C_n^k$. This conjecture is true for $k=3$ as Bouchakour et al. prove working in the context of the Minimum Weight Dominating Set problem (MWDSP) on cycles, i.e, on webs $W_n^1$. They also derive the polynomiality of MWDSP on cycles by giving a polynomial separation algorithm for minor inequalities of $C_n^3$. Although the computational complexity of the separation problem of every minor inequality is still an open problem, we can state that the set covering problem on $C_n^k$ matrices is polynomial for a fixed k and obtain polynomial separation algorithms for particular classes of minor inequalities.     

Dual Characterizations of Set Containments with Strict Convex Inequalities

Characterizations of the containment of a convex set either in an arbitrary convex set or in the complement of a finite union of convex sets (i.e., the set, described by reverse-convex inequalities) are given. These characterizations provide ways of verifying the containments either by comparing their corresponding dual cones or by checking the consistency of suitable associated systems. The convex sets considered in this paper are the solution sets of an arbitrary number of convex inequalities, which can be either weak or strict inequalities. Particular cases of dual characterizations of set containments have played key roles in solving large scale knowledge-based data classification problems where they are used to describe the containments as inequality constraints in optimization problems. The idea of evenly convex set (intersection of open half spaces), which was introduced by W. Fenchel in 1952, is used to derive the dual conditions, characterizing the set containments.     

Valid inequalities for separable concave constraints with indicator variables

We study valid inequalities for optimization models that contain both binary indicator variables and separable concave constraints. These models reduce to a mixed-integer linear program (MILP) when the concave constraints are ignored, or to a nonconvex global optimization problem when the binary restrictions are ignored. In algorithms designed to solve these problems to global optimality, cutting planes to strengthen the relaxation are traditionally obtained using valid inequalities for the MILP only. We propose a technique to obtain valid inequalities that are based on both the MILP constraints and the concave constraints. We begin by characterizing the convex hull of a four-dimensional set consisting of a single binary indicator variable, a single concave constraint, and two linear inequalities. Using this analysis, we demonstrate how valid inequalities for the single node flow set and for the lot-sizing polyhedron can be “tilted” to give valid inequalities that also account for separable concave functions of the arc flows. We present computational results demonstrating the utility of the new inequalities for nonlinear transportation problems and for lot-sizing problems with concave costs. To our knowledge, this is one of the first works that simultaneously convexifies both nonconvex functions and binary variables to strengthen the relaxations of practical mixed-integer nonlinear programs.     

Routing in grid graphs by cutting planes

In this paper we study the following problem, which we call the weighted routing problem. Let be given a graphG = (V, E) with non-negative edge weightsw _{e +} and letN,N 1, be a list of node sets. The weighted routing problem consists in finding mutually disjoint edge setsS ₁,...,S _N such that, for eachk {1, ...,N}, the subgraph (V(S _k),S _k) contains an [s, t]-path for alls, t T _k and the sum of the weights of the edge sets is minimal. Our motivation for studying this problem arises from the routing problem in VLSI-design, where given sets of points have to be connected by wires. We consider the weighted routing problem from a polyhedral point of view. We define an appropriate polyhedron and try to (partially) describe this polyhedron by means of inequalities. We describe our separation algorithms for some of the presented classes of inequalities. Based on these separation routines we have implemented a branch and cut algorithm. Our algorithm is applicable to an important subclass of routing problems arising in VLSI-design, namely to switchbox routing problems where the underlying graph is a grid graph and the list of node sets is located on the outer face of the grid. We report on our computational experience with this class of problem instances.     

On Finite Linear Systems Containing Strict Inequalities

This paper deals with linear systems containing finitely many weak and/or strict inequalities, whose solution sets are referred to as evenly convex polyhedral sets. The classical Motzkin theorem states that every (closed and convex) polyhedron is the Minkowski sum of a convex hull of finitely many points and a finitely generated cone. In this sense, similar representations for evenly convex polyhedra have been recently given by using the standard version for classical polyhedra. In this work, we provide a new dual tool that completely characterizes finite linear systems containing strict inequalities and it constitutes the key for obtaining a generalization of Motzkin theorem for evenly convex polyhedra.     

Worst-case comparison of valid inequalities for the TSP

We consider most of the known classes of valid inequalities for the graphical travelling salesman polyhedron and compute the worst-case improvement resulting from their addition to the subtour polyhedron. For example, we show that the comb inequalities cannot improve the subtour bound by a factor greater than 10/9. The corresponding factor for the class of clique tree inequalities is 8/7, while it is 4/3 for the path configuration inequalities.Research supported in part by Air Force contract F49620-92-J-0125, DARPA contract N00014-92-J-1799 and NSF contract 9302476-CCR.     

Duality for implicit variational problems and numerical applications

The present paper deals with a dual characterization of the solutions of implicit variational problems. Some general results relating the solutions of the dual problem with those of the primal one are applied to variational and quasi-variational inequalities, Nash equilibria, saddle points and fixed points.

Finally a dual method for the numerical solutions of some quasi-variational inequalities is developed.     

Generating All Vertices of a Polyhedron Is Hard

We show that generating all negative cycles of a weighted graph is a hard enumeration problem, in both the directed and undirected cases. More precisely, given a family of negative (directed) cycles, it is an NP-complete problem to decide whether this family can be extended or there are no other negative (directed) cycles in the graph, implying that (directed) negative cycles cannot be generated in polynomial output time, unless P=NP. As a corollary, we solve in the negative two well-known generating problems from linear programming: (i) Given an infeasible system of linear inequalities, generating all minimal infeasible subsystems is hard. Yet, for generating maximal feasible subsystems the complexity remains open. (ii) Given a feasible system of linear inequalities, generating all vertices of the corresponding polyhedron is hard. Yet, in the case of bounded polyhedra the complexity remains open. Equiva lently, the complexity of generating vertices and extreme rays of polyhedra remains open.