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1996, Computers & Chemical Engineering
This paper considers the solution of systems of equations that are expressed by the two sets of equations: a global rectangular system of equations involving more variables than equations, and a set of conditional equations that are expressed as disjunctions. The set of disjunctions are given by equations and inequalities, where the latter define the domain of validity of the equations. In this way the solution of such a system is defined by variables x satisfying the rectangular equations, and exactly one set of equations for each of the disjunctions. This paper focuses mainly in the solution of systems of linear disjunctive equations. Using a convex hull representation of the disjunctions, the disjunctive system of equations is converted into an MELP problem. A sufficient condition is presented under which the model is shown to be solvable as an LP problem. The extension of the proposed method to nonlinear disjunctive equations is also discussed. The application of the proposed algorithms are illustrated with several examples.
Computers & Chemical Engineering, 1998
This paper considers the solution of systems of algebraic equations that are expressed by a global rectangular system of equations, and a set of conditional equations that are expressed as disjunctions. These disjunctions are given by equations and inequalities, where the latter define the domain of validity of the equations. The solution of such a system is defined by variable values satisfying the rectangular equations, and exactly one set of equations for each of the disjunctions This paper addresses first the solution of systems of linear disjunctive equations. Using a convex hull representation of each of the disjunctions, it is shown that these equations can be converted into an MILP problem. A sufficient condition is presented under which this model is shown to be solvable as an LP problem. An extension to nonlinear disjunctive equations is presented by incorporating the proposed MILP formulation within a Newton iterative scheme. The application of the proposed algorithms is illustrated with several examples, including piecewise linear mass balances in process networks, and pipe networks with different flow regimes and check valves.
Information Processing Letters, 1998
We present a technique that transforms any binary programming problem with integral coe cients to a satis ability problem of propositional logic in linear time. Preliminary computational experience using this transformation, shows that a pure logical solver can be a valuable tool for solving binary programming problems. In a number of cases it competes favourably with well known techniques from operations research, especially for hard unsatis able problems.
Computational Optimization and Applications, 2003
Generalized Disjunctive Programming (GDP) has been introduced recently as an alternative to mixed-integer programming for representing discrete/continuous optimization problems. The basic idea of GDP consists of representing these problems in terms of sets of disjunctions in the continuous space, and logic propositions in terms of Boolean variables. In this paper we consider GDP problems involving convex nonlinear inequalities in the
INFORMS Journal on Computing, 2015
In this work, we propose an algorithmic approach to improve mixed-integer models that are originally formulated as convex Generalized Disjunctive Programs (GDP). The algorithm seeks to obtain an improved continuous relaxation of the MILP/MINLP reformulation of the GDP, while limiting the growth in the problem size. There are three main stages that form the basis of the algorithm. The first one is a pre-solve, consequence of the logic nature of GDP, which allows us to reduce the problem size, find good relaxation bounds and identify properties that help us determine where to apply a basic step. The second stage is the iterative application of basic steps, selecting where to apply them, and monitoring the improvement of the formulation. Finally, we use a hybrid reformulation of GDP that seeks to exploit both of the advantages attributed to the two common GDP-to-MILP/MINLP transformations, the Big-M and Hull reformulation. We illustrate the application of this algorithm with several examples. The results show the improvement in the problem formulations by generating models with improved relaxed solutions and relatively small growth in the number of continuous variables and constraints. The algorithm generally leads to reduction in the solution times.
2023
A novel representation is described that models some important NP-hard problems, such as the propositional satisfiability problem (SAT), the Traveling Salesperson Problem (TSP), and the Minimal Set Covering Problem (MSCP) by means of only two types of constraints:'choiceconstraints'and 'exclusion constraints'. In its main section the paper presents an approach for solving a m-CNF-SAT problem (Conjunctive Normal Form Satisfaction: n variables, p clauses, clause length m) by integer programming. The approach is unconventional, because 2n distinct 0-1 variables are used for each clause of the m-CNF-SAT problem. The constraint matrix A forces that for every clause exactly one 0-1 variable is set equal to 1 (choice constraint), and no two 0/1 variables, representing a literal and its complement, are both set equal to 1 (exclusion constraints). The particular m-CNF-SAT instance is coded in a cost vector, which serves for maximization of the number of satisfied clauses. The paper presents a 0/1 Simplex for solving the obtained integer program. A main theorem of the paper is that this algorithm always finds a 0-1 integer solution. A solution of the integer program corresponds to a solution of the m-CNF-SAT and vice versa. The same modelling technique is then used for the Traveling Salesperson Problem and for the Minimal Set Covering: it is shown that a uniform approach is thus useful. Black A., J.A. De Loera, S. Kafer and Laura Sanità [Bla2021] present new pivot rules for the Simplex method for LP over 0/1 polytopes such as ours, that require only polynomial steps in the number of variables, and give the proof. Thus, based on this result and using these pivot rules for our CNF-SAT solver Simplex algorithm, we find a solution in polynomial time. The complexity of CNF-SAT is NP-complete.
2008
The objectives of this paper are to give a brief overview of the code LogMIP and to report numerical experience on a set of test problems. LOGMIP is currently the only code for disjunctive programming, which has implemented the research work done on this area on the last decade. Major motivations in the development of LogMIP have been to facilitate problem formulation of discrete/continuous optimization problems, and to improve the efficiency and robustness of the solution of these problems, particularly for the nonlinear case. LOGMIP is a software system linked to GAMS for solving problems that are formulated as disjunctive/hybrid programs (Vecchietti and Grossmann, 1999). For linear problems the disjunctive/hybrid model can be automatically reformulated as a mixed-integer (MIP) formulation using either a big-M reformulation, or a convex hull reformulation (Balas, 1979) depending on the choice selected by the user. The other option for solving nonlinear problems is the Logic-Based ...
Computational Optimization and Applications, 2020
Nonlinear disjunctive convex sets arise naturally in the formulation or solution methods of many discrete-continuous optimization problems. Often, a tight algebraic representation of the disjunctive convex set is sought, with the tightest such representation involving the characterization of the convex hull of the disjunctive convex set. In the most general case, this can be explicitly expressed through the use of the perspective function in higher dimensional spacethe so-called extended formulation of the convex hull of a disjunctive convex set. However, there are a number of challenges in using this characterization in computation which prevents its wide-spread use, including issues that arise because of the functional form of the perspective function. In this paper, we propose an explicit algebraic representation of a fairly large class of nonlinear disjunctive convex sets using the perspective function that addresses this latter computational challenge. This explicit representation can be used to generate (tighter) algebraic reformulations for a variety of different problems containing disjunctive convex sets, and we report illustrative computational results using this representation for several nonlinear disjunctive problems.
Operations Research, 2011
In this paper, we give a finite disjunctive programming procedure to obtain the convex hull of general mixed-integer linear programs (MILP) with bounded integer variables. We propose a finitely convergent convex hull tree algorithm which constructs a linear program that has the same optimal solution as the associated MILP. In addition, we combine the standard notion of sequential cutting planes with ideas underlying the convex hull tree algorithm to help guide the choice of disjunctions to use within a cutting plane method. This algorithm, which we refer to as the cutting plane tree algorithm, is shown to converge to an integral optimal solution in finitely many iterations. Finally, we illustrate the proposed algorithm on three well-known examples in the literature that require an infinite number of elementary or split disjunctions in a rudimentary cutting plane algorithm.
Computers & Chemical Engineering, 2003
This paper addresses the relaxations in alternative models for disjunctions, big-M and convex hull model, in order to develop guidelines and insights when formulating Mixed-Integer Non-Linear Programming (MINLP), Generalized Disjunctive Programming (GDP), or hybrid models. Characterization and properties are presented for various types of disjunctions. An interesting result is presented for improper disjunctions where results in the continuous space differ from the ones in the mixed-integer space. A cutting plane method is also proposed that avoids the explicit generation of equations and variables of the convex hull. Several examples are presented throughout the paper, as well as a small process synthesis problem, which is solved with the proposed cutting plane method. #
Mixed Integer Nonlinear Programming, 2011
Generalized disjunctive programming (GDP) is an extension of the disjunctive programming paradigm developed by Balas. The GDP formulation involves Boolean and continuous variables that are specified in algebraic constraints, disjunctions and logic propositions, which is an alternative representation to the traditional algebraic mixedinteger programming formulation. After providing a brief review of MINLP optimization, we present an overview of GDP for the case of convex functions emphasizing the quality of continuous relaxations of alternative reformulations that include the big-M and the hull relaxation. We then review disjunctive branch and bound as well as logicbased decomposition methods that circumvent some of the limitations in traditional MINLP optimization. We next consider the case of linear GDP problems to show how a hierarchy of relaxations can be developed by performing sequential intersection of disjunctions. Finally, for the case when the GDP problem involves nonconvex functions, we propose a scheme for tightening the lower bounds for obtaining the global optimum using a combined disjunctive and spatial branch and bound search. We illustrate the application of the theoretical concepts and algorithms on several engineering and OR problems.
Computation, 2021
We study the problem of how to compute the boolean abstraction of the solution set of a linear equation system over the positive reals. We call a linear equation system ϕ exact for the boolean abstraction if the abstract interpretation of ϕ over the structure of booleans is equal to the boolean abstraction of the solution set of ϕ over the positive reals. interpretation over the booleans is thus complete for the boolean abstraction when restricted to exact linear equation systems, while it is not complete more generally. We present a new rewriting algorithm that makes linear equation systems exact for the boolean abstraction while preserving the solutions over the positive reals. The rewriting algorithm is based on the elementary modes of the linear equation system. The computation of the elementary modes may require exponential time in the worst case, but is often feasible in practice with freely available tools. For exact linear equation systems, we can compute the boolean abstrac...
2007
Abstract Optimized solvers for the Boolean satisfiability (SAT) problem have many applications in areas such as hardware and software verification, FPGA routing, planning, and so forth. Further uses are complicated by the need to express" counting constraints" in conjunctive normal form (CNF). Expressing such constraints by pure CNF leads to more complex SAT instances.
Knowledge-Based Systems, 2003
Nowadays, many real problems can be modelled as Constraint Satisfaction Problems (CSPs). Some CSPs are considered non-binary disjunctive CSPs. Many researchers study the problems of deciding consistency for Disjunctive Linear Relations (DLRs). In this paper, we propose a new class of constraints called Extended DLRs consisting of disjunctions of linear inequalities, linear disequations and non-linear disequations. This new class of constraints extends the class of DLRs. We propose a heuristic algorithm called DPOLYSA that solves Extended DLRs, as a non-binary disjunctive CSP solver. This proposal works on a polyhedron whose vertices are also polyhedra that represent the non-disjunctive problems. We also present a statistical preprocessing step which translates the disjunctive problem into a non-disjunctive and ordered one in each step.
In press, 2002
Nowadays, many real problems can be modelled as Constraint Satisfaction Problems (CSPs). Some CSPs are considered nonbinary disjunctive CSPs. Many researchers study the problems of deciding consistency for Disjunctive Linear Relations (DLRs). In this paper, we propose a new class of constraints called Extended DLRs consisting of disjunctions of linear inequalities, linear disequations and non-linear disequations. This new class of constraints extends the class of DLRs. We propose a heuristic algorithm called DPOLYSA that solves Extended DLRs, as a non-binary disjunctive CSP solver. This proposal works on a polyhedron whose vertices are also polyhedra that represent the nondisjunctive problems. We also present a statistical preprocessing step which translates the disjunctive problem into a non-disjunctive and ordered one in each step.
Information Sciences, 2010
This paper presents a new method for solving systems of Boolean equations. The method is based on converting the equations so that we operate in the integer domain. In the integer domain better and more efficient methodologies for solving equations are available. The conversion leads us to a system of polynomial equations obeying certain characteristics. A method is proposed for solving these equations. The most computationally demanding step is the repeated multiplication of polynomials. We develop a method for this problem that is significantly faster than the standard approach. We also introduce another variant of the method, the so-called hybrid approach, that leads to reduced memory requirements. Theoretical and experimental results indicate the superior performance of the proposed method and its variant compared to the competing methods. The proposed method is also validated by applying it to the problem of hardware verification.
Computers & Chemical Engineering, 2005
Raman and Grossmann [Raman, R., & Grossmann, I.E. (1994). Modeling and computational techniques for logic based integer programming.
Algorithms
This work is a short review of the state of the art aiming to contribute to the use, disclosure, and propagation of systems of linear inequalities in real life, teaching, and research. It shows that the algebraic structure of their solutions consists of the sum of a linear subspace, an acute cone, and a polytope, and that adequate software exists to obtain, in their simplest forms, these three components. The work describes, based on orthogonality and polarity, homogeneous and complete systems of inequalities, the associated compatibility problems, and their relations with convex polyhedra and polytopes, which are the only possible solution for bounded problems, the most common in real practice. The compatibility and the observability problems, including their symbolic forms, are analyzed and solved, identifying the subsets of unknowns with unique solutions and those unbounded, important items of information with practical relevance in artificial intelligence and automatic learning....
European Journal of Operational Research, 2016
In this work, we present a Lagrangean relaxation of the hull-reformulation of discrete-continuous optimization problems formulated as linear generalized disjunctive programs (GDP). The proposed Lagrangean relaxation has three important properties. The first property is that it can be applied to any linear GDP. The second property is that the solution to its continuous relaxation always yields 0-1 values for the binary variables of the hull-reformulation. Finally, it is simpler to solve than the continuous relaxation of the hull-reformulation. The proposed Lagrangean relaxation can be used in different GDP solution methods. In this work, we explore its use as primal heuristic to find feasible solutions in a disjunctive branch and bound algorithm. The modified disjunctive branch and bound is tested with several instances. The results show that the proposed disjunctive branch and bound performs better than other versions of the algorithm that do not include this primal heuristic.
We describe the basic notions and algorithm of the mixed boolean-algebraic solver being developed in the
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