New formulations for the uncapacitated multiple allocation hub location problem

In this paper we review the integer linear formulations of the uncapacitated multiple allocation hub location problem, we study the scope of validity of these formulations and give new ones that generalize the older formulations. Our formulations allow one or two visits to hubs and include a more general cost structure that needs not satisfy the triangle inequality. We prove that the constraints defined by cliques of a related (intersection) graph are tighter constraints than the classical ones. We also discuss a pre-processing of the problem, which is very useful for reducing its size. Finally, we check the strength of the new formulations and compare them with others in the literature by solving instances of two commonly used data sets: the CAB (Civil Aeronautics Board) and AP (Australian Post), and also randomly generated instances. Our computational results clearly show that our formulations outperform all others previously used for small and medium problems.     

Parametric multiple sequence alignment and phylogeny construction

Bounds are given on the size of the parameter-space decomposition induced by multiple sequence alignment problems where phylogenetic information may be given or inferred. It is shown that many of the usual formulations of these problems fall within the same integer parametric framework, implying that the number of distinct optima obtained as the parameters are varied across their ranges is polynomially bounded in the length and number of sequences.     

An optimization approach to multiple sequence alignment

The problem of multiple sequence alignment is recast as an optimization problem using Markov decision theory. One seeks to minimize the expected or average cost of alignment subject to data-derived constraints. In this setting, the problem is equivalent to a linear program which can be solved efficiently using modern interior-point methods. We present numerical results from an implementation of the algorithm for protein sequence alignment     

DCA based algorithms for multiple sequence alignment (MSA)

In the last years many techniques in bioinformatics have been developed for the central and complex problem of optimally aligning biological sequences. In this paper we propose a new optimization approach based on DC (Difference of Convex functions) programming and DC Algorithm (DCA) for the multiple sequence alignment in its equivalent binary linear program, called “Maximum Weight Trace” problem. This problem is beforehand recast as a polyhedral DC program with the help of exact penalty techniques in DC programming. Our customized DCA, requiring solution of a few linear programs, is original because it converges after finitely many iterations to a binary solution while it works in a continuous domain. To scale-up large-scale (MSA), a constraint generation technique is introduced in DCA. Preliminary computational experiments on benchmark data show the efficiency of the proposed algorithm DCAMSA, which generally outperforms some standard algorithms.     

New formulations of the Hop-Constrained Minimum Spanning Tree problem via Miller-Tucker-Zemlin constraints

Given an undirected network with positive edge costs and a natural number p, the Hop-Constrained Minimum Spanning Tree problem (HMST) is the problem of finding a spanning tree with minimum total cost such that each path starting from a specified root node has no more than p hops (edges). In this paper, we develop new formulations for HMST. The formulations are based on Miller-Tucker-Zemlin (MTZ) subtour elimination constraints, MTZ-based liftings in the literature offered for HMST, and a new set of topology-enforcing constraints. We also compare the proposed models with the MTZ-based models in the literature with respect to linear programming relaxation bounds and solution times. The results indicate that the new models give considerably better bounds and solution times than their counterparts in the literature and that the new set of constraints is competitive with liftings to MTZ constraints, some of which are based on well-known, strong liftings of Desrochers and Laporte (1991).     

New formulations and solution procedures for the hop constrained network design problem

Optical fiber provides tremendous advantages in being able to carry a wide range of services including video on demand, video conferencing, distance learning, remote medical imaging, and telecommuting. The high capacities encourage carriers to create networks that are substantially sparser than previous copper based networks. A recent publication by the Telecommunications Industry Association indicated that investment in fiber optics is projected to reach $35 billion in the year 2003. Given the magnitude of investments, the design of networks becomes a very important issue. Most telecommunication companies (telcos), IT consulting companies, network equipment manufacturers and network service providers have extensive network design groups. The primary function of these groups is to design the most efficient networks both in terms of costs and performance and maintain them. These designers need flexible tools to support topological network design decisions. These decisions involve significant levels of investments in transmissions and switching facilities, and impact the resulting networks’ performance fundamentally.In this paper we study a special type of a network design problem called the hop constrained backbone network design problem. We present new mathematical programming formulations of the problem and develop an efficient solution procedure based on the linear programming relaxation. Extensive computational results across a number of networks are reported.     

New formulations and valid inequalities for a bilevel pricing problem

Consider the problem of maximizing the toll revenue collected on a multi-commodity transportation network. This fits a bilevel framework where a leader sets tolls, while users respond by selecting cheapest paths to their destination. We propose novel formulations of the problem, together with valid inequalities yielding improved algorithms.     

Strong formulations for the pooling problem

The pooling problem is a well-studied global optimization problem with applications in oil refining and petrochemical industry. Despite the strong NP-hardness of the problem, which is proved formally in this paper, most instances from the literature have recently been solved efficiently by use of strong formulations. The main contribution from this paper is a new formulation that proves to be stronger than other formulations based on proportion variables. Moreover, we propose a promising branching strategy for the new formulation and provide computational experiments confirming the strength of the new formulation and the effectiveness of the branching strategy.     

Integer-friendly formulations for the r-separation problem

In this paper, we consider different formulations for the r-separation problem, where the objective is to choose as as many points as possible from a given set of points subject to the constraint that no two selected points can be closer than r units to one another. Our goal is to devise a mathematical programming formulation with an LP-relaxation which yields integer solutions with great frequency. We consider six different formulations of the r-separation problem. We show that the LP-relaxations of the most obvious formulations will yield fractional results in all instances of the problem if an optimal solution contains fewer than half of the given points. To build computationally effective formulations for the r-separation problem, we write dense constraints with unit right-hand-sides. The LP formulation that performs the best in our computational tests almost always finds 0–1 solutions to the problem.     

Extended formulations for the A-cut problem

LetG=(V, E) be an undirected graph andA⊆V. We consider the problem of finding a minimum cost set of edges whose deletion separates every pair of nodes inA. We consider two extended formulations using both node and edge variables. An edge variable formulation has previously been considered for this problem (Chopra and Rao (1991), Cunningham (1991)). We show that the LP-relaxations of the extended formulations are stronger than the LP-relaxation of the edge variable formulation (even with an extra class of valid inequalities added). This is interesting because, while the LP-relaxations of the extended formulations can be solved in polynomial time, the LP-relaxation of the edge variable formulation cannot. We also give a class of valid inequalities for one of the extended formulations. Computational results using the extended formulations are performed.     

New sequence spaces in multiple normed spaces

In this work, using lacunary sequences and the notion of ideal convergence we define and examine new sequence spaces with respect to a sequence of modulus functions in n-normed linear spaces. Further, the definition of I_θ-convergence in n-normed linear spaces and some related results are given.     

Second-kind integral formulations of the capacitance problem

The standard approach to calculating electrostatic forces and capacitances involves solving a surface integral equation of the first kind. However, discretizations of this problem lead to ill-conditioned linear systems and second-kind integral equations usually solve for the dipole density, which can not be directly related to electrostatic forces. This paper describes a second-kind equation for the monopole or charge density and investigates different discretization schemes for this integral formulation. Numerical experiments, using multipole accelerated matrix–vector multiplications, demonstrate the efficiency and accuracy of the new approach. This revised version was published online in June 2006 with corrections to the Cover Date.     

Network-based formulations of the quadratic assignment problem

We present two formulations of the Quadratic Assignment Problem (QAP) that result in network flow problems with integer variables and side constraints. A linearization of the QAP is obtained in both cases by considering each facility to consist of two parts—a source for all outgoing flows from that facility, and a sink for all incoming flows to the facility. Preliminary computational experience with both approaches is presented.     

Regular expression constrained sequence alignment

We introduce regular expression constrained sequence alignment as the problem of finding the maximum alignment score between given strings $S_1$ and $S_2$ over all alignments such that in these alignments there exists a segment where some substring $s_1$ of $S_1$ is aligned to some substring $s_2$ of $S_2$, and both $s_1$ and $s_2$ match a given regular expression R, i.e. $s_1,s_2\in L(R)$ where $L(R)$ is the regular language described by R. For complexity results we assume, without loss of generality, that $n=|S_1|⩾|m|=|S_2|$. A motivation for the problem is that protein sequences can be aligned in a way that known motifs guide the alignments. We present an $\mathrm{O}(nmr)$ time algorithm for the regular expression constrained sequence alignment problem where $r=\mathrm{O}(t^4)$, and t is the number of states of a nondeterministic finite automaton N that accepts $L(R)$. We use in our algorithm a nondeterministic weighted finite automaton M that we construct from N. M has $\mathrm{O}(t^2)$ states where the transition-weights are obtained from the given costs of edit operations, and state-weights correspond to optimum alignment scores we compute using the underlying dynamic programming solution for sequence alignment. If we are given a deterministic finite automaton D accepting $L(R)$ with $t_d$ states then our construction creates a deterministic finite automaton $M_d$ with $t_d^2$ states. In this case, our algorithm takes $\mathrm{O}(t_d^{2}nm)$ time. Using $M_d$ results in faster computation than using M when $t_d<t^2$. If we only want to compute the optimum score, the space required by our algorithm is $\mathrm{O}(t^{2}n)$ ($\mathrm{O}(t_d^{2}m)$ if we use a given $M_d$). If we also want to compute an optimal alignment then our algorithm uses $\mathrm{O}(t^{2}m+t^2|s_1||s_2|)$ space ($\mathrm{O}(t_d^{2}m+t_d^2|s_1||s_2|)$ space if we use a given $M_d$) where $s_1$ and $s_2$ are substrings of $S_1$ and $S_2$, respectively, $s_1,s_2\in L(R)$, and $s_1$ and $s_2$ are aligned together in the optimal alignment that we construct. We also show that our method generalizes for the case of the problem with affine gap penalties, and for finding optimal regular expression constrained local sequence alignments.     

New integer programming formulations and an exact algorithm for the ordered cutting stock problem

Apart from trim loss minimization, there are many other issues concerning cutting processes that arise in real production systems. One of these is related to the number of stacks that need to be opened near the cutting machines. Many researchers have worked in the last years on cutting stock problems with additional constraints on the number of open stacks. In this paper, we address a related problem: the Ordered Cutting Stock Problem (OCSP). In this case, a stack is opened for every new client's order, and it is closed only when all the items of that order are cut. The OSCP has been introduced recently in the literature. Our aim is to provide further insight into this problem. This paper describes three new integer programming formulations for solving it, and an exact algorithm based on column generation, branch-and-bound and cutting planes. We report on computational experiments on a set of random instances. The results show that good lower bounds can be computed quickly, and that optimal solutions can be found in a reasonable amount of time.     

On relaxations of the max k-cut problem formulations

A tight continuous relaxation is a crucial factor in solving mixed integer formulations of many NP-hard combinatorial optimization problems. The (weighted) max k-cut problem is a fundamental combinatorial optimization problem with multiple notorious mixed integer optimization formulations. In this paper, we explore four existing mixed integer optimization formulations of the max k-cut problem. Specifically, we show that the continuous relaxation of a binary quadratic optimization formulation of the problem is: (i) stronger than the continuous relaxation of two mixed integer linear optimization formulations and (ii) at least as strong as the continuous relaxation of a mixed integer semidefinite optimization formulation. We also conduct a set of experiments on multiple sets of instances of the max k-cut problem using state-of-the-art solvers that empirically confirm the theoretical results in item (i). Furthermore, these numerical results illustrate the advances in the efficiency of global non-convex quadratic optimization solvers and more general mixed integer nonlinear optimization solvers. As a result, these solvers provide a promising option to solve combinatorial optimization problems. Our codes and data are available on GitHub.     

LP formulations of the shortest path tree problem

Based on a pair of primal-dual LP formulations of the shortest path tree problem, the first algorithmic approach to reoptimizing the shortest paths subject to changes in the edge weights was proposed by S. Pallottino and M.G. Scutellá in 2003. We shall here focus solely on their introductory sections, propose some simplifications of the models considered, and finally relate the resulting models to the presentation of single-source shortest path problems in textbooks treating this subject with but limited or no reference to LP.Received: April 2004, Revised: August 2004, MSC classification: 90C05, 90C35, 90B10 Dedicated to the memory of Stefano Pallottino     

On formulations of the stochastic uncapacitated lot-sizing problem

We consider two formulations of a stochastic uncapacitated lot-sizing problem. We show that by adding (?,S) inequalities to the one with the smaller number of variables, both formulations give the same LP bound. Then we show that for two-period problems, adding another class of inequalities gives the convex hull of integral solutions.