A New Decomposition Technique in Solving Multistage Stochastic Linear Programs by Infeasible Interior Point Methods

Multistage stochastic linear programming (MSLP) is a powerful tool for making decisions under uncertainty. A deterministic equivalent problem of MSLP is a large-scale linear program with nonanticipativity constraints. Recently developed infeasible interior point methods are used to solve the resulting linear program. Technical problems arising from this approach include rank reduction and computation of search directions. The sparsity of the nonanticipativity constraints and the special structure of the problem are exploited by the interior point method. Preliminary numerical results are reported. The study shows that, by combining the infeasible interior point methods and specific decomposition techniques, it is possible to greatly improve the computability of multistage stochastic linear programs.     

An Infeasible Mizuno–Todd–Ye Type Algorithm for Convex Quadratic Programming with Polynomial Complexity

Infeasible interior point methods have been very popular and effective. In this paper, we propose a predictor–corrector infeasible interior point algorithm for convex quadratic programming, and we prove its convergence and analyze its complexity. The algorithm has the polynomial numerical complexity with O(nL)-iteration.     

Convergence of the homotopy path for a full-Newton step infeasible interior-point method

Roos [C. Roos, A full-Newton step O(n) infeasible interior-point algorithm for linear optimization. SIAM J. Optim. 16 (4) (2006) 1110-1136 (electronic)] proposed a new primal-dual infeasible interior-point method for linear optimization. This new method can be viewed as a homotopy method. In this work, we show that the homotopy path has precisely one accumulation point in the optimal set. Moreover, this accumulation point is the analytic center of a subset of the optimal set and depends on the starting point of the infeasible interior-point method.     

Analysis of infeasible-interior-point paths arising with semidefinite linear complementarity problems

We consider semidefinite monotone linear complementarity problems (SDLCP) in the space ⁿ of real symmetric n×n-matrices equipped with the cone ⁿ₊ of all symmetric positive semidefinite matrices. One may define weighted (using any Mⁿ₊₊ as weight) infeasible interior point paths by replacing the standard condition XY=rI, r>0, (that defines the usual central path) by (XY+YX)/2=rM. Under some mild assumptions (the most stringent is the existence of some strictly complementary solution of (SDLCP)), these paths have a limit as r0, and they depend analytically on all path parameters (such as r and M), even at the limit point r=0.Mathematics Subject Classification (1991): 90C33, 65K05     

An infeasible (exterior point) simplex algorithm for assignment problems

The so called Modified Hung—Rom Algorithm, based upon theoretical considerations of Hirsch-paths, seems to be one of the most efficient algorithms for assignment problems. Since any two basic feasible solutions to a linear problem can always be connected with a short simplex path passing through the infeasible region, development of algorithms based upon theoretical considerations on infeasible paths seems to be of great practical interest. This paper presents an algorithm of this kind for assignment problems.     

一种求解约束优化问题的改进差分进化算法

针对在处理约束优化问题时约束条件难以处理的问题,提出了一种求解约束优化问题的改进差分进化算法.即在每代进化前将群体分为可行个体和不可行个体两类,对不可行个体,用差量法将其逐个转化为可行个体,并保持种群规模不变,经过一序列的进化后,计算所有可行个体的适应度并找到问题的最优解.对5个经典函数进行了优化测试,测试结果表明提出的算法对求解约束优化问题是有效的.     

A Full-Newton Step Infeasible Interior-Point Algorithm for Linear Programming Based on a Kernel Function

This paper proposes an infeasible interior-point algorithm with full-Newton step for linear programming, which is an extension of the work of Roos (SIAM J. Optim. 16(4):1110–1136, 2006). The main iteration of the algorithm consists of a feasibility step and several centrality steps. We introduce a kernel function in the algorithm to induce the feasibility step. For parameter p∈[0,1], the polynomial complexity can be proved and the result coincides with the best result for infeasible interior-point methods, that is, O(nlog n/ε). This work was supported in part by the National Natural Science Foundation of China under Grant No. 10871098.     

Using intermediate infeasible solutions to approach vehicle routing problems with precedence and loading constraints

Logistics and transportation issues have been receiving increasing attention during the last decades and their requirements have gradually changed, making it necessary to take into account new situations and conditions. The Double Traveling Salesman Problem with Multiple Stacks (DTSPMS) is a pickup and delivery problem in which some additional precedence and loading constraints are imposed on the vehicle to be used. In this paper we approach the problem using intermediate infeasible solutions to diversify the search process and we develop some fixing procedures and infeasibility measures to deal with this kind of solutions and take advantage of their potential.     

A New Infeasible Interior-Point Method Based on a Non-Coercive Kernel Function with Improved Centering Steps for Second-Order Cone Optimization

In this article, we present a new full Nesterov-Todd step infeasible interior-point method for second-order cone optimization based on a non-coercive kernel function. The main iteration consists of a so-called feasibility step and one centering step, whereas the earlier versions, in [4 S. Bouali and S. Kabbaj ( 2014 ). Full-NT step infeasible interior-point method for SOCO based on a specific kernel function . Afr. Mat. 25 : 549 – 565 .[Crossref] , [Google Scholar], 21 M. Zangiabadi , G. Gu , and C. Roos ( 2013 ). A full Nesterov-Todd step infeasible interior-point method for second-order cone optimization . J. Optim. Theory Appl. 158 : 816 – 858 .[Crossref], [Web of Science ®] , [Google Scholar]], needed two additional centering steps. We use a kernel function to induce the feasibility step. The new algorithm reduces the searching steps in each iteration and tenders an interesting analysis for complexity bound.