On Minimal Valid Inequalities for Mixed Integer Conic Programs

We study disjunctive conic sets involving a general regular (closed, convex, full dimensional, and pointed) cone K such as the nonnegative orthant, the Lorentz cone or the positive semidefinite cone. In a unified framework, we introduce K-minimal inequalities and show that under mild assumptions, these inequalities together with the trivial cone-implied inequalities are sufficient to describe the convex hull. We study the properties of K-minimal inequalities by establishing algebraic necessary conditions for an inequality to be K-minimal. This characterization leads to a broader algebraically defined class of K- sublinear inequalities. We establish a close connection between K-sublinear inequalities and the support functions of sets with a particular structure. This connection results in practical ways of showing that a given inequality is K-sublinear and K-minimal. Our framework generalizes some of the results from the mixed integer linear case. It is well known that the minimal inequalities for mixed integer linear programs are generated by sublinear (positively homogeneous, subadditive and convex) functions that are also piecewise linear. This result is easily recovered by our analysis. Whenever possible we highlight the connections to the existing literature. However, our study unveils that such a cut generating function view treating the data associated with each individual variable independently is not possible in the case of general cones other than nonnegative orthant, even when the cone involved is the Lorentz cone

On packing and covering polyhedra in infinite dimensions

We consider the natural generalizations of packing and covering polyhedra in infinite dimensions, and study issues related to duality and integrality of extreme points for these sets. Using appropriate finite truncations we give conditions under which complementary slackness holds for primal/dual pairs of the infinite linear programming problems associated with infinite packing and covering polyhedra. We also give conditions under which the extreme points are integral. We illustrate an application of our results on an infinite-horizon lot-sizing problem. Keywords: Covering polyhedron; Packing polyhedron; Infinite linear program; Complementary slackness; Integral extreme poin

Analysis and Simplex-type Algorithms for Countably Infinite Linear Programming Models of Markov Decision Processes.

The class of Markov decision processes (MDPs) provides a popular framework which covers a wide variety of sequential decision-making problems. We consider infinite-horizon discounted MDPs with countably infinite state space and finite action space. Our goal is to establish theoretical properties and develop new solution methods for such MDPs by studying their linear programming (LP) formulations. The LP formulations have countably infinite numbers of variables and constraints and therefore are called countably infinite linear programs (CILPs). General CILPs are challenging to analyze or solve, mainly because useful theoretical properties and techniques of finite LPs fail to extend to general CILPs. Another goal of this thesis is to deepen the limited current understanding of CILPs, resulting in new algorithmic approaches to find their solutions. Recently, Ghate and Smith (2013) developed an implementable simplex-type algorithm for solving a CILP formulation of a non-stationary MDP with finite state space. We establish rate of convergence results for their simplex algorithm with a particular pivoting rule and another existing solution method for such MDPs, and compare empirical performance of the algorithms. We also present ways to accelerate their simplex algorithm. The class of non-stationary MDPs with finite state space can be considered to be a subclass of stationary MDPs with countably infinite state space. We present a simplex-type algorithm for solving a CILP formulation of a stationary MDP with countably infinite state space that is implementable (using only finite data and computation in each iteration). We show that the algorithm finds a sequence of policies that improves monotonically and converges to optimality in value, and present a numerical illustration. An important extension of MDPs considered so far are constrained MDPs, which optimize an objective function while satisfying constraints, typically on budget, quality, and so on. For constrained non-stationary MDPs with finite state space, we provide a necessary and sufficient condition for a feasible solution of its CILP formulation to be an extreme point. Since simplex-type algorithms are expected to navigate between extreme points, this result sets a foundation for developing a simplex-type algorithm for constrained non-stationary MDPs.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113486/1/ilbinlee_1.pd

On the Relationship Between the Value Function and the Efficient Frontier of a Mixed Integer Linear Optimization Problem

In this paper, we investigate the connection between the efficient frontier (EF) of a general multiobjective mixed integer linear optimization problem (MILP) and the so-called restricted value function (RVF) of a closely related single-objective MILP. We demonstrate that the EF of the multiobjective MILP is comprised of points on the boundary of the epigraph of the RVF so that any description of the EF suffices to describe the RVF and vice versa. In the first part of the paper, we describe the mathematical structure of the RVF, including characterizing the set of points at which it is differentiable, the gradients at such points, and the subdifferential at all nondifferentiable points. Because of the close relationship of the RVF to the EF, we observe that methods for constructing so-called value functions and methods for constructing the EF of a multiobjective optimization problem, each of which have been developed in separate communities, are effectively interchangeable. By exploiting this relationship, we propose a generalized cutting plane algorithm for constructing the EF of a multiobjective MILP based on a generalization of an existing algorithm for constructing the classical value function. We prove that the algorithm is finite under a standard boundedness assumption and comes with a performance guarantee if terminated early

Robust Solutions to Uncertain Multiobjective Programs

Decision making in the presence of uncertainty and multiple conflicting objec-tives is a real-life issue, especially in the fields of engineering, public policy making, business management, and many others. The conflicting goals may originate from the variety of ways to assess a system’s performance such as cost, safety, and affordability, while uncertainty may result from inaccurate or unknown data, limited knowledge, or future changes in the environment. To address optimization problems that incor-porate these two aspects, we focus on the integration of robust and multiobjective optimization. Although the uncertainty may present itself in many different ways due to a diversity of sources, we address the situation of objective-wise uncertainty only in the coefficients of the objective functions, which is drawn from a finite set of scenarios. Among the numerous concepts of robust solutions that have been proposed and de-veloped, we concentrate on a strict concept referred to as highly robust efficiency in which a feasible solution is highly robust efficient provided that it is efficient with respect to every realization of the uncertain data. The main focus of our study is uncertain multiobjective linear programs (UMOLPs), however, nonlinear problems are discussed as well. In the course of our study, we develop properties of the highly robust efficient set, provide its characterization using the cone of improving directions associated with the UMOLP, derive several bound sets on the highly robust efficient set, and present a robust counterpart for a class of UMOLPs. As various results rely on the polar and strict polar of the cone of improving directions, as well as the acuteness of this cone, we derive properties and closed-form representations of the (strict) polar and also propose methods to verify the property of acuteness. Moreover, we undertake the computation of highly robust efficient solutions. We provide methods for checking whether or not the highly robust efficient set is empty, computing highly robust efficient points, and determining whether a given solution of interest is highly robust efficient. An application in the area of bank management is included