On optimizing over lift-and-project closures

The lift-and-project closure is the relaxation obtained by computing all lift-and-project cuts from the initial formulation of a mixed integer linear program or equivalently by computing all mixed integer Gomory cuts read from all tableau's corresponding to feasible and infeasible bases. In this paper, we present an algorithm for approximating the value of the lift-and-project closure. The originality of our method is that it is based on a very simple cut generation linear programming problem which is obtained from the original linear relaxation by simply modifying the bounds on the variables and constraints. This separation LP can also be seen as the dual of the cut generation LP used in disjunctive programming procedures with a particular normalization. We study some properties of this separation LP in particular relating it to the equivalence between lift-and-project cuts and Gomory cuts shown by Balas and Perregaard. Finally, we present some computational experiments and comparisons with recent related works

Exploiting Structures in Mixed-Integer Second-Order Cone Optimization Problems for Branch-and-Conic-Cut Algorithms

This thesis studies computational approaches for mixed-integer second-order cone optimization (MISOCO) problems. MISOCO models appear in many real-world applications, so MISOCO has gained significant interest in recent years. However, despite recent advancements, there is a gap between the theoretical developments and computational practice. Three chapters of this thesis address three areas of computational methodology for an efficient branch-and-conic-cut (BCC) algorithm to solve MISOCO problems faster in practice. These chapters include a detailed discussion on practical work on adding cuts in a BCC algorithm, novel methodologies for warm-starting second-order cone optimization (SOCO) subproblems, and heuristics for MISOCO problems.The first part of this thesis concerns the development of a novel warm-starting method of interior-point methods (IPM) for SOCO problems. The method exploits the Jordan frames of an original instance and solves two auxiliary linear optimization problems. The solutions obtained from these problems are used to identify an ideal initial point of the IPM. Numerical results on public test sets indicate that the warm-start method works well in practice and reduces the number of iterations required to solve related SOCO problems by around 30-40%.The second part of this thesis presents novel heuristics for MISOCO problems. These heuristics use the Jordan frames from both continuous relaxations and penalty problems and present a way of finding feasible solutions for MISOCO problems. Numerical results on conic and quadratic test sets show significant performance in terms of finding a solution that has a small gap to optimality.The last part of this thesis presents application of disjunctive conic cuts (DCC) and disjunctive cylindrical cuts (DCyC) to asset allocation problems (AAP). To maximize the benefit from these powerful cuts, several decisions regarding the addition of these cuts are inspected in a practical setting. The analysis in this chapter gives insight about how these cuts can be added in case-specific settings

Topics in exact precision mathematical programming

The focus of this dissertation is the advancement of theory and computation related to exact precision mathematical programming. Optimization software based on floating-point arithmetic can return suboptimal or incorrect resulting because of round-off errors or the use of numerical tolerances. Exact or correct results are necessary for some applications. Implementing software entirely in rational arithmetic can be prohibitively slow. A viable alternative is the use of hybrid methods that use fast numerical computation to obtain approximate results that are then verified or corrected with safe or exact computation. We study fast methods for sparse exact rational linear algebra, which arises as a bottleneck when solving linear programming problems exactly. Output sensitive methods for exact linear algebra are studied. Finally, a new method for computing valid linear programming bounds is introduced and proven effective as a subroutine for solving mixed-integer linear programming problems exactly. Extensive computational results are presented for each topic.Ph.D.Committee Chair: Dr. William J. Cook; Committee Member: Dr. George Nemhauser; Committee Member: Dr. Robin Thomas; Committee Member: Dr. Santanu Dey; Committee Member: Dr. Shabbir Ahmed; Committee Member: Dr. Zonghao G

Combinatorial Optimization and Integer Programming

Solution techniques for combinatorial optimization and integer programming problems are core disciplines in operations research with contributions of mathematicians as well as computer scientists and economists. This article surveys the state of the art in solving such problems to optimality

Computing with Multi-Row Intersection Cuts

Cutting planes are one of the main techniques currently used to solve large-scale Mixed-Integer Linear Programming (MIP) models. Many important cuts used in practice, such as Gomory Mixed-Integer (GMI) cuts, are obtained by solving the linear relaxation of the MIP, extracting a single row of the simplex tableau, then applying integrality arguments to it. A natural extension, which has received renewed attention, is to consider cuts that can only be generated when considering multiple rows of the simplex tableau simultaneously. Although the theoretical importance of such multi-row cutting planes has been proved in a number of works, their effective use in practice remains a challenge. Since the entire class of multi-row cuts proves challenging to separate, one approach to obtain them is the following. First, the integral non-basic variables are fixed to zero. Then, a lattice-free set, which induces an intersection cut, is generated. Finally, the cut coefficients for the integral non-basic variables are computed by the so-called trivial lifting procedure. In this thesis, we address some computational aspects of this approach, and we make three novel contributions. In our first contribution, we describe a small subset of multi-row intersection cuts based on the infinity norm, which works for relaxations with arbitrary numbers of rows. We present an algorithm to generate them and run extensive computational experiments to evaluate their effectiveness. We conclude that these cuts yield benefits comparable to using the entire class of multi-row cuts, but at a small fraction of the computational cost. In our second contribution, we describe a practical method for performing the trivial lifting step on relaxations with two rows. Unlike previous methods, our method is applicable to intersection cuts derived from any lattice-free set, and, for maximal lattice-free sets, it is guaranteed to run in constant time. Computational experiments confirm that the algorithm is at least two orders of magnitudes faster than current alternatives. In our final contribution, we revisit single-row relaxations containing a single integral non-basic variable, with the goal of obtaining inequalities that are not dominated by GMI cuts. The novelty in our approach is that we use the framework of intersection cuts and trivial lifting, which allows us to obtain a geometric interpretation of our cuts, a fast algorithm for generating them, and an upper bound on their split rank. We run computational experiments and conclude that, for a few instances, they close considerably more gap than GMI cuts alone