In this thesis, we address problems from two topics of applied mathematics: linear integer programming and polyhedral computation. Linear integer programming concerns solving optimisation problems to maximise a linear cost function over the set of integer points in a polyhedron. Polyhedral computation is concerned with algorithms for computing different properties of convex polyhedra. First, we explore the theory and computation of Gröbner bases and Markov bases for linear integer programming. Second, we investigate and improve an algorithm from polyhedral computation that converts between different representations of cones and polyhedra. A Markov basis is a set of integer vectors such that we can move between any two feasible solutions of an integer program by adding or subtracting vectors in the Markov basis while never moving outside the set of feasible solutions. Markov bases are mainly used in algebraic statistics for sampling from a set of feasible solutions. The major contribution of this thesis is a fast algorithm for computing Markov bases, which we used to solve a previously intractable computational challenge. Gröbner basis methods are exact local search approaches for solving integer programs. We present a Gröbner basis approach that can use the structure of an integer program in order to solve it more efficiently. Gröbner basis methods are interesting mainly from a purely theoretical viewpoint, but they are also interesting because they may provide insight into why some classes of integer programs are difficult to solve using standard techniques and because someday they may be able to solve these difficult problems. Computing the properties of convex polyhedra is useful for solving problems within different areas of mathematics such as linear programming, integer programming, combinatorial optimisation, and computational geometry. We investigate and improve an algorithm for converting between a generator representation of a cone or polyhedron and a constraint representation of the cone or polyhedron and vice versa. This algorithm can be extended to compute circuits of matrices, which are used in computational biology for metabolic pathway analysis.

We propose an approach to address data uncertainty for discrete optimization problems that allows controlling the degree of conservatism of the solution, and is computationally tractable both practically and theoretically. When both the cost coefficients and the data in the constraints of an integer programming problem are subject to uncertainty, we propose a robust integer programming problem of moderately larger size that allows to control the degree of conservatism of the solution in terms of probabilistic bounds on constraint violation. When only the cost coefficients are subject to uncertainty and the problem is a 0 - 1 discrete optimization problem on n variables, then we solve the robust counterpart by solving n + 1 instances of the original problem. Thus, the robust counterpart of a polynomially solvable 0 -1 discrete optimization problem remains polynomially solvable. Moreover, we show that the robust counterpart of an NP-hard α-approximable 0 - 1 discrete optimization problem remains α-approximal. / Singapore-MIT Alliance (SMA)

The prediction of energetically favorable side-chain conformations is a fundamental element in homology modeling of proteins and the design of novel protein sequences. The space of side-chain conformations can be approximated by a discrete space of probabilistically representative side-chain conformations (called rotamers). The problem is, then, to find a rotamer selection for each amino acid that minimizes a potential energy function. This is called the Global Minimum Energy Conformation (GMEC) problem. This problem is an NP-hard optimization problem. The Dead-End Elimination theorem together with the A* algorithm (DEE/A*) has been successfully applied to this problem. However, DEE fails to converge for some complex instances. In this paper, we explore two alternatives to DEE/A* in solving the GMEC problem. We use a probabilistic inference method, the max-product (MP) belief-propagation algorithm, to estimate (often exactly) the GMEC. We also investigate integer programming formulations to obtain the exact solution. There are known ILP formulations that can be directly applied to the GMEC problem. We review these formulations and compare their effectiveness using CPLEX optimizers. We also present preliminary work towards applying the branch-and-price approach to the GMEC problem. The preliminary results suggest that the max-product algorithm is very effective for the GMEC problem. Though the max-product algorithm is an approximate method, its speed and accuracy are comparable to those of DEE/A* in large side-chain placement problems and may be superior in sequence design. / Singapore-MIT Alliance (SMA)

This thesis investigates a new approach to lattice basis reduction suggested by M. Seysen. Seysen's algorithm attempts to globally reduce a lattice basis, whereas the Lenstra, Lenstra, Lovasz (LLL) family of reduction algorithms concentrates on local reductions. We show that Seysen's algorithm is well suited for reducing certain classes of lattice bases, and often requires much less time in practice than the LLL algorithm. We also demonstrate how Seysen's algorithm for basis reduction may be applied to subset sum problems. Seysen's technique, used in combination with the LLL algorithm, and other heuristics, enables us to solve a much larger class of subset sum problems than was previously possible.

We propose a novel branch-and-price (B&P) approach to solve the maximum weighted independent set problem (MWISP). Our approach uses clones of vertices to create edge-disjoint partitions from vertex-disjoint partitions. We solve the MWISP on sub-problems based on these edge-disjoint partitions using a B&P framework, which coordinates sub-problem solutions by involving an equivalence relationship between a vertex and each of its clones. We present test results for standard instances and randomly generated graphs for comparison. We show analytically and computationally that our approach gives tight bounds and it solves both dense and sparse graphs quite quickly.

We consider combinatorial t-design problems as discrete optimization problems. Our motivation is that only a few studies have been done on the use of exact optimization techniques in designs, and that classical methods in design theory have still left many open existence questions. Roughly defined, t-designs are pairs of discrete sets that are related following some strict properties of size, balance, and replication. These highly structured relationships provide optimal solutions to a variety of problems in computer science like error-correcting codes, secure communications, network interconnection, design of hardware; and are applicable to other areas like statistics, scheduling, games, among others. We give a new approach to combinatorial t-designs that is useful in constructing t-designs by polyhedral methods. The first contribution of our work is a new result of equivalence of t-design problems with a graph theory problem. This equivalence leads to a novel integer programming formulation for t-designs, which we call GDP. We analyze the polyhedral properties of GDP and conclude, among other results, the associated polyhedron dimension. We generate new classes of valid inequalities to aim at approximating this integer program by a linear program that has the same optimal solution. Some new classes of valid inequalities are generated as Chv´atal-Gomory cuts, other classes are generated by graph complements and combinatorial arguments, and others are generated by the use of incidence substructures in a t-design. In particular, we found a class of valid inequalities that we call stable-set class that represents an alternative graph equivalence for the problem of finding a t-design. We analyze and give results on the strength of these new classes of valid inequalities. We propose a separation problem and give its integer programming formulation as a maximum (or minimum) edge-weight biclique subgraph problem. We implement a pure cutting-plane algorithm using one of the stronger classes of valid inequalities derived. Several instances of t-designs were solved efficiently by this algorithm at the root node of the search tree. Also, we implement a branch-and-cut algorithm and solve several instances of 2-designs trying different base formulations. Computational results are included.

Presburger arithmetic is the first-order theory of the integers with addition and ordering, but without multiplication. This theory is decidable and the sets it defines admit several different representations, including formulas, generators, and finite automata, the latter being the focus of this thesis. Finite-automata representations of Presburger sets work by encoding numbers as words and sets by automata-defined languages. With this representation, set operations are easily computable as automata operations, and minimized deterministic automata are a canonical representation of Presburger sets. However, automata-based representations are somewhat opaque and do not allow all operations to be performed efficiently. An ideal situation would be to be able to move easily between formula-based and automata-based representations but, while building an automaton from a formula is a well understood process, moving the other way is a much more difcult problem that has only attracted attention fairly recently. The main results of this thesis are new algorithms for extracting information about Presburger-definable sets represented by finite automata. More precisely, we present algorithms that take as input a finite-automaton representing a Presburger definable set S and compute in polynomial time the affine hull over Q or over Z of the set S, i.e., the smallest set defined by a conjunction of linear equations (and congruence relations in Z) which includes S. Also, we present an algorithm that takes as input a deterministic finite-automaton representing the integer elements of a polyhedron P and computes a quantifier-free formula corresponding to this set. The algorithms rely on a very detailed analysis of the scheme used for encoding integer vectors and this analysis sheds light on some structural properties of finite-automata representing Presburger definable sets. The algorithms presented have been implemented and the results are encouraging : automata with more than 100000 states are handled in seconds.

Consider the problem of maximizing the revenue generated by tolls set on a subset of arcs of a transportation network, where origin-destination flows (commodities) are assigned to shortest paths with respect to the sum of tolls and initial costs. This thesis is concerned with a particular case of the above problem, in which all toll arcs are connected and constitute a path, as occurs on highways. Further, as toll levels are usually computed using the highway entry and exit points, a complete toll subgraph is considered, where each toll arc corresponds to a toll subpath. Two variants of the problem are studied, with or without specific constraints linking together the tolls on the arcs. The problem is modelled as a linear mixed integer program, and proved to be NP-hard. Next, several classes of valid inequalities are proposed, which strengthen important constraints of the initial model. Their efficiency is first shown theoretically, as these are facet defining for the restricted one and two commodity problems. Also, we prove that some of the valid inequalities proposed, together with several constraints of the linear program, provide a complete description of the convex hull of feasible solutions for a single commodity problem. Numerical tests have also been conducted, and highlight the real efficiency of the valid inequalities for the multi-commodity case. Finally, we point out the links between the problem studied in the thesis and a more classical design and pricing problem in economics. / Considérons le problème qui consiste à maximiser les profits issus de la tarification d’un sous-ensemble d’arcs d’un réseau de transport, où les flots origine-destination (produits) sont affectés aux plus courts chemins par rapport aux tarifs et aux coûts initiaux. Cette thèse porte sur une structure de réseau particulière du problème ci-dessus, dans laquelle tous les arcs tarifables sont connectés et forment un chemin, comme c’est le cas sur une autoroute. Étant donné que les tarifs sont habituellement déterminés selon les points d’entrée et de sortie sur l’autoroute, nous considérons un sous-graphe tarifable complet, où chaque arc correspond en réalité à un sous-chemin. Deux variantes de ce problème sont étudiées, avec ou sans contraintes spécifiques reliant les niveaux de tarifs sur les arcs. Ce problème peut être modélisé comme un programme linéaire mixte entier. Nous prouvons qu’il est NP-difficile. Plusieurs familles d’inégalités valides sont ensuite proposées, celles-ci renforçant certaines contraintes du modèle initial. Leur efficacité est d’abord démontrée de manière théorique, puisqu’il s’agit de facettes des problèmes restreints à un ou deux produits. Certaines des inégalités valides proposées, ainsi que plusieurs contraintes du modèle initial, permettent aussi de donner une description complète de l’enveloppe convexe des solutions réalisables d’un problème restreint à un seul produit. Des tests numériques ont également été menés, et mettent en évidence l’efficacité réelle des inégalités valides pour le problème général à plusieurs produits. Enfin, nous soulignons les liens entre le problème de tarification de réseau étudié dans cette thèse et un problème plus classique de tarification de produits en gestion.

In this thesis, we introduce the Partitioning-Hub Location-Routing problem (PHLRP), which can be classied as a variant of the hub location problem. PHLRP consists of partitioning a network into sub-networks, locating at least one hub in each subnetwork and routing the traffic within the network such that all inter-subnetwork traffic is routed through the hubs and all intra-subnetwork traffic stays within the sub-networks all the way from the source to the destination. Obviously, besides the hub location component, PHLRP also involves a graph partitioning component and a routing component. PHLRP finds applications in the strategic planning or deployment of the Intermediate System-Intermediate System (ISIS) Internet Protocol networks and the Less-than-truck load freight distribution systems. First, we introduce three IP formulations for solving PHLRP. The hub location component and the graph partitioning components of PHLRP are modeled in the same way in all three formulations. More precisely, the hub location component is represented by the p-median variables and constraints; and the graph partitioning component is represented by the size-constrained graph partitioning variables and constraints. The formulations differ from each other in the way the peculiar routing requirements of PHLRP are modeled. We then carry out analytical and empirical comparisons of the three IP formulations. Our thorough analysis reveals that one of the formulations is provably the tightest of the three formulations. We also show analytically that the LP relaxations of the other two formulations do not dominate each other. On the other hand, our empirical comparison in a standard branch-and-cut framework that is provided by CPLEX shows that not the tightest but the most compact of the three formulations yield the best performance in terms of solution time. From this point on, based on the insight gained from detailed analysis of the formulations, we focus our attention on a common sub-problem of the three formulations: the so-called size-constrained graph partitioning problem. We carry out a detailed polyhedral analysis of this problem. The main benet from this polyhedral analysis is that the facets we identify for the size-constrained graph partitioning problem constitute strong valid inequalities for PHLRP. And finally, we wrap up our efforts for solving PHLRP. Namely, we present the results of our computational experiments, in which we employ some facets of the size-constrained graph partitioning polytope in a branch-and-cut algorithm for solving PHLRP. Our experiments show that our approach brings signicant improvements to the solution time of PHLRP when compared with the default branch-and-cut solver of XPress. / Dans cette thèse, nous introduisons le problème Partitionnement-Location des Hubs et Acheminement (PLHA), une variante du problème de location de hubs. Le problème PLHA partitionne un réseau afin d'obtenir des sous-réseaux, localise au moins un hub dans chaque sous-réseau et achemine le traffic dans le réseau de la maniére suivante : le traffic entre deux sous-réseaux distincts doit être éxpedié au travers des hubs tandis que le traffic entre deux noeuds d'un même sous-réseau ne doit pas sortir de celui-ci. PLHA possède des applications dans le planning stratégique, ou déploiement, d'un certain protocole de communication utilisé dans l'Internet, Intermediate System - Intermediate System, ainsi que dans la distribution des frets. Premièrement, nous préesentons trois formulations linéaires en variables entières pour résoudre PLHA. Le partitionnement du graphe et la localisation des hubs sont modélisées de la même maniére dans les trois formulations. Ces formulations diffèrent les unes des autres dans la maniére dont l'acheminement du traffic est traité. Deuxièmement, nous présentons des comparaisons analytiques et empiriques des trois formulations. Notre comparaison analytique démontre que l'une des formulations est plus forte que les autres. Néanmoins, la comparaison empirique des formulations, via le solveur CPLEX, montre que la formulation la plus compacte (mais pas la plus forte) obtient les meilleures performances en termes de temps de résolution du problème. Ensuite, nous nous concentrons sur un sous-problème, à savoir, le partitionnement des graphes sous contrainte de taille. Nous étudions le polytope des solutions réalisables de ce sous-problème. Les facettes de ce polytope constituent des inégalités valides fortes pour PLHA et peuvent être utilisées dans un algorithme de branch-and-cut pour résoudre PLHA. Finalement, nous présentons les résultats d'un algorithme de branch-and-cut que nous avons développé pour résoudre PLHA. Les résultats démontrent que la performance de notre méthode est meilleure que celle de l'algorithme branch-and-cut d'Xpress.

This thesis consists of an introduction and sevenindependent, but closely related, papers which all deal withproblems in structural optimization. In particular, we considermodels and methods for global optimization of problems intopology design of discrete and continuum structures. In the first four papers of the thesis the nonconvex problemof minimizing the weight of a truss structure subject to stressconstraints is considered. First itis shown that a certainsubclass of these problems can equivalently be cast as linearprograms and thus efficiently solved to global optimality.Thereafter, the behavior of a certain well-known perturbationtechnique is studied. It is concluded that, in practice, thistechnique can not guarantee that a global minimizer is found.Finally, a convergent continuous branch-and-bound method forglobal optimization of minimum weight problems with stress,displacement, and local buckling constraints is developed.Using this method, several problems taken from the literatureare solved with a proof of global optimality for the firsttime. The last three papers of the thesis deal with topologyoptimization of discretized continuum structures. Theseproblems are usually modeled as mixed or pure nonlinear 0-1programs. First, the behavior of certain often usedpenalization methods for minimum compliance problems isstudied. It is concluded that these methods may fail to producea zero-one solution to the considered problem. To remedy this,a material interpolation scheme based on a rational functionsuch that compli- ance becomes a concave function is proposed.Finally, it is shown that a broad range of nonlinear 0-1topology optimization problems, including stress- anddisplacement-constrained minimum weight problems, canequivalently be modeled as linear mixed 0-1 programs. Thisresult implies that any of the standard methods available forgeneral linear integer programming can now be used on topologyoptimization problems. <b>Keywords:</b>topology optimization, global optimization,stress constraints, linear programming, mixed integerprogramming, branch-and-bound.