A strong conic quadratic reformulation for machine-job assignment with controllable processing times

We describe a polynomial-size conic quadratic reformulation for a machine-job assignment problem with separable convex cost. Because the conic strengthening is based on only the objective of the problem, it can also be applied to other problems with similar cost functions. Computational results demonstrate the effectiveness of the conic reformulation. CitationAppeared in Operations Research Letters. … Read more

The Flow Set with Partial Order

The flow set with partial order is a mixed-integer set described by a budget on total flow and a partial order on the arcs that may carry positive flow. This set is a common substructure of resource allocation and scheduling problems with precedence constraints and robust network flow problems under demand/capacity uncertainty. We give a … Read more

Partition Inequalities for Capacitated Survivable Network Design Based on Directed P-Cycles

We study the design of capacitated survivable networks using directed p-cycles. A p-cycle is a cycle with at least three arcs, used for rerouting disrupted flow during edge failures. Survivability of the network is accomplished by reserving sufficient slack on directed p-cycles so that if an edge fails, its flow can be rerouted along the … Read more

An O(n^2) Algorithm for Lot Sizing with Inventory Bounds and Fixed Costs

Lot-sizing problems with inventory bounds and fixed charges have not received much attention in the literature, even though there are many emerging applications in which highly specialized storage is the main activity of interest. The traditional infinite capacity and variable cost assumptions on inventory that have been prevalent in the literature are inappropriate in situations … Read more

Max-min separability: incremental approach and application to supervised data classification

A new algorithm for the computation of a piecewise linear function separating two finite point sets in $n$-dimensional space is developed and the algorithm is applied to solve supervised data classification problems. The algorithm computes hyperplanes incrementally and it finds as many hyperplanes as necessary to separate two sets with respect to some tolerance. An … Read more

A new lower bound for one-machine earliness-tardiness scheduling

In one-machine scheduling, MIP time-indexed formulations are often used to provide very good lower bounds through Lagrangian relaxations. In order to get an improved lower bound, we add valid cuts to a time-indexed formulation and show we still have a Lagrangian relaxation that can be solved as a shortest path in a graph. Two branch-and-bound … Read more

Semidefinite Representation of Convex Sets

Let $S =\{x\in \re^n:\, g_1(x)\geq 0, \cdots, g_m(x)\geq 0\}$ be a semialgebraic set defined by multivariate polynomials $g_i(x)$. Assume $S$ is convex, compact and has nonempty interior. Let $S_i =\{x\in \re^n:\, g_i(x)\geq 0\}$, and $\bdS$ (resp. $\bdS_i$) be the boundary of $S$ (resp. $S_i$). This paper, as does the subject of semidefinite programming (SDP), concerns … Read more

Minimum weight t-composition of an integer

If $p \geq t$ are positive integers, a t-composition of p is an ordered t-tuple of positive integers summing p. If $T=(s_1, s_2, \dots, s_t)$ is a t-composition of p and W is a $p-(t-1) \times t$ matrix, call $W(T)= \sum_{k=1}^t w_{s_k k}$ the weight of the t-composition T. We show that finding a minimum … Read more

A globally convergent trust-region SQP method without a penalty function for nonlinearly constrained optimization

In this paper, we propose a new trust-region SQP method, which uses no penalty function, for solving nonlinearly constrained optimization problem. Our method consists of alternate two phases. Specifically, we alternately proceed the feasibility restoration phase and the objective function minimization phase. The global convergence property of the proposed method is shown. CitationCooperative Research Report … Read more

A primal-dual interior point method for nonlinear semidefinite programming

In this paper, we consider a primal-dual interior point method for solving nonlinear semidefinite programming problems. By combining the primal barrier penalty function and the primal-dual barrier function, a new primal-dual merit function is proposed within the framework of the line search strategy. We show the global convergence property of our method. Finally some numerical … Read more