Feasibility and solvability of Lyapunov-type linear programming over symmetric cones

This paper describes efficient algorithms for determining how buffer space should be allocated in a flow line. We analyze two problems: a primal problem, which minimizes total buffer space subject to a production rate constraint; and a dual problem, which maximizes production rate subject to a total buffer space constraint. The dual problem is solved by means of a gradient method, and the primal problem is solved using the dual solution. Numerical results are presented. Profit optimization problems are natural generalizations of the primal and dual problems, and we show how they can be solved using essentially the same algorithms.

3 - Further Topics in Linear Programming

In this paper we study the problem of minimizing total weighted tardiness, a proxy for maximizing on-time delivery performance, on parallel nonidentical batch processing machines. We first formulate the (primal) problem as a nonlinear integer programming model. We then show that the primal problem can be solved exactly by solving a corresponding dual problem with a nonlinear relaxation. Since both the primal and the dual problems are NP-hard, we use genetic algorithms, based on random keys and multiple choice encodings, to heuristically solve them. We find that the genetic algorithms consistently outperform a standard mathematical programming package in terms of solution quality and computation time. We also compare the smaller problem instances to a breadth-first tree search algorithm that gives evidence of the quality of the solutions.

A neural net algorithm for multidimensional maximum entropy spectrum estimation

Nonlinear optimization problems have two different representations, the primal problem and the dual problem. The relation between the primal and the dual problem is provided by an elegant duality theory. This chapter presents the basics of duality theory. Section 4.1 discusses the primal problem and the perturbation function. Section 4.2 presents the dual problem. Section 4.3 discusses the weak and strong duality theorems, while section 4.4 discusses the duality gap. This section presents the formulation of the primal problem, the definition and properties of the perturbation function, the definition of stable primal problem, and the existence conditions of optimal multiplier vectors.

Positive and doubly stochastic maps, and majorization in Euclidean Jordan algebras

This chapter states the Monge–Kantorovich problem and provides the duality result in a fairly general setting. The primal problem is interpreted as the central planner's problem of determining the optimal assignment of workers to firms, while the dual problem is interpreted as the invisible hand's problem of obtaining a system of decentralized equilibrium prices. In general, the primal problem always has a solution (which means that an optimal assignment of workers to jobs exists), but the dual does not: the optimal assignment cannot always be decentralized by a system of prices. However, the cases where the dual problem does not have a solution are rather pathological, and in all of the cases considered in the rest of the book, both the primal and the dual problems have solutions.

Abstract. In this paper, some omissions in Mishra and Lai [13], havebeen pointed out and their corrective measures have been discussed briefly. 1. IntroductionA pair of primal and dual problems in mathematical programming is calledsymmetric if the dual of the dual is the primal problem. Dorn [6] introducedthe concept of symmetric duality in quadratic programming. His results wereextended to nonlinear convex programming problems by Dantzig et al. [4] andlater by Bazaraa and Goode [3] over arbitrary cones.Mangasarian [11] introduced the concept of second-order duality for non-linear problems. Since then, many authors [1, 2, 7, 9, 15, 16] have workedon second-order symmetric duality. Mishra and Lai [13] studied Mond-Weirtype second-order multiobjective symmetric duality for the following pair ofproblems:(P) K−minimize f(x,y)−12p T ∇ yy f(x,y)psubject to −∇ y (λ T f)(x,y) −∇ yy (λ T f)(x,y) ∈ C ∗2 ,y T [∇ y (λ T f)(x,y) +∇ yy (λ T f)(x,y)] > 0,λ ∈ K ∗ , x ∈ C 1 .(D) K−maximize f(u,v)−12q

In this paper we generalize the major results of Andersson and Perlman on LCI models to the setting of symmetric cones and give an explicit closed form formula for the estimate of the covariance matrix in the generalized LCI models that we define. To this end, we replace the cone $H_I^+(\mathbb{R})$ sitting inside the Jordan algebra of symmetric real $I \times I$-matrices by the symmetric cone $\Omega$ of an Euclidean Jordan algebra $V$. We introduce the Andersson-Perlman cone $\Omega(\mathscr{K}\subseteq\Omega$ which generalizes $\mathscr{P}(\mathscr{K})\subseteq H_I^+(\mathscr{R})$. We prove several characterizations and properties of $\Omega(\mathscr{K})$ which allows us to recover, though with different proofs, the main results of Andersson and Perlman regarding $\mathscr{P}(\mathscr{K})$. The new lattice conditional independence models are defined, assuming that the Euclidean Jordan algebra $V$ has a symmetric representation. Using standard results from the theory of Jordan algebras, we can reduce the general model to the case where $V$ is the Jordan algebra of Hermitian matrices over the real, complex or quaternionic numbers, and $\Omega$ is the corresponding cone of positive-definite matrices. Our main statistical result is a closed-form formula for the estimate of the covariance matrix in the generalized LCI model. We also give the likelihood ratio test for testing a given model versus another one, nested within the first.

In this paper, we consider a least square semidefinite programming problem under ellipsoidal data uncertainty. We show that the robustification of this uncertain problem can be reformulated as a semidefinite linear programming problem with an additional second-order cone constraint. We then provide an explicit quantitative sensitivity analysis on how the solution under the robustification depends on the size/shape of the ellipsoidal data uncertainty set. Next, we prove that, under suitable constraint qualifications, the reformulation has zero duality gap with its dual problem, even when the primal problem itself is infeasible. The dual problem is equivalent to minimizing a smooth objective function over the Cartesian product of second-order cones and the Euclidean space, which is easy to project onto. Thus, we propose a simple variant of the spectral projected gradient method (Birgin et al. in SIAM J. Optim. 10:1196---1211, 2000) to solve the dual problem. While it is well-known that any accumulation point of the sequence generated from the algorithm is a dual optimal solution, we show in addition that the dual objective value along the sequence generated converges to a finite value if and only if the primal problem is feasible, again under suitable constraint qualifications. This latter fact leads to a simple certificate for primal infeasibility in situations when the primal feasible set lies in a known compact set. As an application, we consider robust correlation stress testing where data uncertainty arises due to untimely recording of portfolio holdings. In our computational experiments on this particular application, our algorithm performs reasonably well on medium-sized problems for real data when finding the optimal solution (if exists) or identifying primal infeasibility, and usually outperforms the standard interior-point solver SDPT3 in terms of CPU time.

In This Chapter We Introduce Euclidean Jordan Algebras. As Will Be Seen In Chapter Viii, These are the same as the formally real Jordan algebras more usually considered in the literature. For such algebras we prove a ‘spectral theorem’, which, in the case of the algebra of real symmetric matrices, specializes to the usual spectral theorem. Then we prove a fundamental result which establishes a one-to-one correspondence between symmetric cones and Euclidean Jordan algebras. A consequence of this is the unique decomposition of every symmetric cone into a direct product of irreducible ones. Finally, for symmetric cones, we give an independent description in Jordan algebra terms of all the geometric and analytic objects introduced in Chapter I.

Self-scaled barrier functions on self-scaled cones were axiomatically introduced by Nesterov and Todd in 1994 as a tool for the construction of primal—dual long-step interior point algorithms. This paper provides firm foundations for these objects by exhibiting their symmetry properties, their close ties with the symmetry groups of their domains of definition, and subsequently their decomposition into irreducible parts and their algebraic classification theory. In the first part we recall the characterization of the family of self-scaled cones as the set of symmetric cones and develop a primal—dual symmetric viewpoint on self-scaled barriers, results that were first discovered by the second author. We then show in a short, simple proof that any pointed, convex cone decomposes into a direct sum of irreducible components in a unique way, a result which can also be of independent interest. We then proceed to showing that any self-scaled barrier function decomposes, in an essentially unique way, into a direct sum of self-scaled barriers defined on the irreducible components of the underlying symmetric cone. Finally, we present a complete algebraic classification of self-scaled barrier functions using the correspondence between symmetric cones and Euclidean—Jordan algebras.

Stochastic linear–quadratic control problems with affine constraints

Duality is an essential part of LP theory. It is related to the special relationship between one LP problem and another, both of which involve the same data (A, b, c), but positioned differently (except for the self-duality, see below). The former is referred to as primal problem, and the latter as dual problem. Based on duality, once a primal problem is solved, so is the dual problem, and vice versa. This chapter is devoted to such topics as the relationship between their feasible regions as well as optimal solutions, optimality condition, dual simplex algorithm in tableau form and in revised form, shadow price, and dual elimination. Before making notes on the duality in the final section, we introduce a special bilevel LP problem together with a related approach to intercepting the optimal set.

SummaryThis paper is concerned with the optimal control problem governed by linear parabolic equation with box constraints on control variables. We employ the Fenchel duality scheme to derive an unconstrained dual problem. Compared with the primal problem, the objective functional of the dual problem includes a projection onto the box constraints. We prove the existence and uniqueness of solutions to the dual problem and derive the first‐order optimality conditions. Furthermore, we investigate the saddle point property between the solutions of the primal problem and the solutions of the dual problem. To solve the dual problem, we design two implementable methods: the conjugate gradient method and the semi‐smooth Newton method. The solutions of the primal problem can be easily obtained through the solutions of the dual problem. We demonstrate the effectiveness and accuracy of the proposed methods by solving three example problems.