Warren P. Adams

A hierarchy of relaxation between the continuous and convex hull representations

In this paper a reformulation technique is presented that takes a given linear zero-one programming problem, converts it into a zero-one polynomial programming problem, and then relinearizes it into an extended linear program. It is shown that the strength of the resulting reformulation depends on the degree of the terms used to produce the polynomial program at the intermediate step of this method. In fact, as this degree varies from one up to the number of variables in the problem, a hierarchy of sharper representations is obtained with the final relaxation representing the convex hull of feasible solutions. The reformulation technique readily extends to produce a similar hierarchy of linear relaxations for zero-one polynomial programming problems. A characterization of the convex hull in the original variable space is also available through a projection process. The structure of this convex hull characterization (or its other relaxations) can be exploited to generate strong or facetial valid inequalities through appropriate surrogates in a computational framework. The surrogation process can also be used to study various classes of facets for different combinatorial optimization problems. Some examples are given to illustrate this point. 1. Introduction. This paper describes a technique for generating a hierarchy of polyhedral representations for linear and polynomial zero-one programming problems.In this paper a reformulation technique is presented that takes a given linear zero-one programming problem, converts it into a zero-one polynomial programming problem, and then relinearizes it into an extended linear program. It is shown that the strength of the resulting reformulation depends on the degree of the terms used to produce the polynomial program at the intermediate step of this method. In fact, as this degree varies from one up to the number of variables in the problem, a hierarchy of sharper representations is obtained with the final relaxation representing the convex hull of feasible solutions. The reformulation technique readily extends to produce a similar hierarchy of linear relaxations for zero-one polynomial programming problems. A characterization of the convex hull in the original variable space is also available through a projection process. The structure of this convex hull characterization (or its other relaxations) can be exploited to generate strong or facetial valid inequaliti...

A hierarchy of relaxations and convex hull characterizations for mixed-integer zero-one programming problems

This paper is concerned with the generation of tight equivalent representations for mixedinteger zero-one programming problems. For the linear case, we propose a technique which first converts the problem into a nonlinear, polynomial mixed-integer zero-one problem by multiplying the constraints with some suitable d-degree polynomial factors involving the n binary variables, for any given d E (0, . . . , n}, and subsequently linearizes the resulting problem through appropriate variable transformations. As d varies from zero to n, we obtain a hierarchy of relaxations spanning from the ordinary linear programming relaxation to the convex hull of feasible solutions. The facets of the convex hull of feasible solutions in terms of the original problem variables are available through a standard projection operation. We also suggest an alternate scheme for applying this technique which gives a similar hierarchy of relaxations, but involving fewer “complicating” constraints. Techniques for tightening intermediate level relaxations, and insights and interpretations within a disjunctive programming framework are also presented. The methodology readily extends to multilinear mixed-integer zero-one polynomial programming problems in which the continuous variables appear linearly in the problem.

Linearization Strategies for a Class of Zero-One Mixed Integer Programming Problems

This paper is concerned with a new linearization strategy for a class of zero-one mixed integer programming problems that contains quadratic cross-product terms between continuous and binary variables, and between the binary variables themselves. This linearization scheme provides an equivalent mixed integer linear programming problem which yields a tighter continuous relaxation than that obtainable via the alternative linearization techniques available in the literature. Moreover, the proposed technique provides a unifying framework in the sense that all the alternate methods lead to formulations that are accessible through appropriate surrogates of the constraints of the new linearized formulation. Extensions to various other types of mixed integer nonlinear programming problems are also discussed.

Exploiting Special Structures in Constructing a Hierarchy of Relaxations for 0-1 Mixed Integer Problems

A new hierarchy of relaxations is presented that provides a unifying framework for constructing a spectrum of continuous relaxations spanning from the linear programming relaxation to the convex hull representation for linear mixed integer 0-1 problems. This hierarchy is an extension of the Reformulation-Linearization Technique (RLT) of Sherali and Adams (1990, 1994a), and is particularly designed to exploit special structures. Specifically, inherent special structures are exploited by identifying particular classes of multiplicative factors that are applied to the original problem to reformulate it as an equivalent polynomial programming problem, and subsequently, this resulting problem is linearized to produce a tighter relaxation in a higher dimensional space. This general framework permits us to generate an explicit hierarchical sequence of tighter relaxations leading up to the convex hull representation. (A similar hierarchy can be constructed for polynomial mixed integer 0-1 problems.) Additional ideas for further strengthening RLT-based constraints by using conditional logical implications, as well as relationships with sequential lifting, are also explored. Several examples are presented to demonstrate how underlying special structures, including generalized and variable upper bounding, covering, partitioning, and packing constraints, as well as sparsity, can be exploited within this framework. For some types of structures, low level relaxations are exhibited to recover the convex hull of integer feasible solutions.

Mixed-integer bilinear programming problems

This paper addresses a class of problems called mixed-integer bilinear programming problems. These problems are identical to the well known bilinear programming problems with the exception that one set of variables is restricted to be binary valued, and they arise in various production, location—allocation, and distribution application contexts. We first identify some special cases of this problem which are relatively more readily solvable, even though their continuous relaxations are still nonconvex. For the more general case, we employ a linearization technique and design a composite Lagrangian relaxation-implicit enumeration-cutting plane algorithm. Extensive computational experience is provided to test the efficacy of various algorithmic strategies and the effects of problem data on the computational effort of the proposed algorithm.

A level-2 reformulation-linearization technique bound for the quadratic assignment problem

Abstract This paper studies polyhedral methods for the quadratic assignment problem. Bounds on the objective value are obtained using mixed 0–1 linear representations that result from a reformulation–linearization technique (rlt). The rlt provides different “levels” of representations that give increasing strength. Prior studies have shown that even the weakest level-1 form yields very tight bounds, which in turn lead to improved solution methodologies. This paper focuses on implementing level-2. We compare level-2 with level-1 and other bounding mechanisms, in terms of both overall strength and ease of computation. In so doing, we extend earlier work on level-1 by implementing a Lagrangian relaxation that exploits block-diagonal structure present in the constraints. The bounds are embedded within an enumerative algorithm to devise an exact solution strategy. Our computer results are notable, exhibiting a dramatic reduction in nodes examined in the enumerative phase, and allowing for the exact solution of large instances.

Comparisons and enhancement strategies for linearizing mixed 0-1 quadratic programs

We present a linearization strategy for mixed 0-1 quadratic programs that produces small formulations with tight relaxations. It combines constructs from a classical method of Glover and a more recent reformulation-linearization technique (RLT). By using binary identities to rewrite the objective, a variant of the first method results in a concise formulation with the level-1 RLT strength. This variant is achieved as a modified surrogate dual of a Lagrangian subproblem to the RLT. Special structures can be exploited to obtain reductions in problem size, without forfeiting strength. Preliminary computational experience demonstrates the potential of the new representations.

A simple recipe for concise mixed 0-1 linearizations

A new linearization method for mixed 0-1 polynomial programs is obtained by repeatedly applying a classical strategy introduced almost 30 years ago. Two important contributions are: the most concise known linear representations of cubic and higher-degree problems, and a simple argument for explaining and extending two alternate linearizations.

A Hierarchy of Relaxations Leading to the Convex Hull Representation for General Discrete Optimization Problems

We consider linear mixed-integer programs where a subset of the variables are restricted to take on a finite number of general discrete values. For this class of problems, we develop a reformulation-linearization technique (RLT) to generate a hierarchy of linear programming relaxations that spans the spectrum from the continuous relaxation to the convex hull representation. This process involves a reformulation phase in which suitable products using a defined set of Lagrange interpolating polynomials (LIPs) are constructed, accompanied by the application of an identity that generalizes x(1−x) for the special case of a binary variable x. This is followed by a linearization phase that is based on variable substitutions. The constructs and arguments are distinct from those for the mixed 0-1 RLT, yet they encompass these earlier results. We illustrate the approach through some examples, emphasizing the polyhedral structure afforded by the linearized LIPs. We also consider polynomial mixed-integer programs, exploitation of structure, and conditional-logic enhancements, and provide insight into relationships with a special-structure RLT implementation.

Linear forms of nonlinear expressions: New insights on old ideas

We show how recent linearization methods for mixed 0-1 polynomial programs can be improved and unified through an interpretation of classical techniques. We consider quadratic expressions involving the product of a linear function and a binary variable, and extensions having products of binary variables. Computational results are reported.