A Branch and Bound algorithm for the minimax regret spanning arborescence

The paper considers the problem of finding a spanning arborescence on a directed network whose arc costs are partially known. It is assumed that each arc cost can take on values from a known interval defining a possible economic scenario. In this context, the problem of finding the spanning arborescence which better approaches to that of minimum overall cost under each possible scenario is studied. The minimax regret criterion is proposed in order to obtain such a robust solution of the problem. As it is shown, the bounds on the optimal value of the minimax regret optimization problem obtained in a previous paper, can be used here in a Branch and Bound algorithm in order to give an optimal solution. The computational behavior of the algorithm is tested through numerical experiments. This research has been supported by the Spanish Ministry of Education and Science and FEDER Grant No. MTM2006-04393 and by the European Alfa Project, “Engineering System for Preparing and Making Decisions Under Multiple Criteria”, II-0321-FA.     

Minimization Algorithms Based on Supervisor and Searcher Cooperation

In the present work, we explore a general framework for the design of new minimization algorithms with desirable characteristics, namely, supervisor-searcher cooperation. We propose a class of algorithms within this framework and examine a gradient algorithm in the class. Global convergence is established for the deterministic case in the absence of noise and the convergence rate is studied. Both theoretical analysis and numerical tests show that the algorithm is efficient for the deterministic case. Furthermore, the fact that there is no line search procedure incorporated in the algorithm seems to strengthen its robustness so that it tackles effectively test problems with stronger stochastic noises. The numerical results for both deterministic and stochastic test problems illustrate the appealing attributes of the algorithm.     

武器-目标分配问题算法研究综述

介绍武器-目标分配问题算法研究的现状及进展.目前解决WTA问题的算法主要是以一种智能算法为主结合另外一种或者多种智能算法的混合优化算法,并且不断有新的智能算法和一些新技术、新思想相结合的算法出现.指出了目前WTA问题算法研究中存在的一些不足及进一步的发展方向.     

An inexact column-and-constraint generation method to solve two-stage robust optimization problems

We propose a new inexact column-and-constraint generation (i-C&CG) method to solve two-stage robust optimization problems. The method allows solutions to the master problems to be inexact, which is desirable when solving large-scale and/or challenging problems. It is equipped with a backtracking routine that controls the trade-off between bound improvement and inexactness. Importantly, this routine allows us to derive theoretical finite convergence guarantees for our i-C&CG method. Numerical experiments demonstrate computational advantages of our i-C&CG method over state-of-the-art column-and-constraint generation methods.     

Numerical Comparison of Some Penalty-Based Constraint Handling Techniques in Genetic Algorithms

We study five penalty function-based constraint handling techniques to be used with genetic algorithms in global optimization. Three of them, the method of superiority of feasible points, the method of parameter free penalties and the method of adaptive penalties have already been considered in the literature. In addition, we introduce two new modifications of these methods. We compare all the five methods numerically in 33 test problems and report and analyze the results obtained in terms of accuracy, efficiency and reliability. The method of adaptive penalties turned out to be most efficient while the method of parameter free penalties was the most reliable.     

Minmax regret location–allocation problem on a network under uncertainty

We consider a robust location–allocation problem with uncertainty in demand coefficients. Specifically, for each demand point, only an interval estimate of its demand is known and we consider the problem of determining where to locate a new service when a given fraction of these demand points must be served by the utility. The optimal solution of this problem is determined by the “minimax regret” location, i.e., the point that minimizes the worst-case loss in the objective function that may occur because a decision is made without knowing which state of nature will take place. For the case where the demand points are vertices of a network we show that the robust location–allocation problem can be solved in O(min{p, n − p}n³m) time, where n is the number of demand points, p (p < n) is the fixed number of demand points that must be served by the new service and m is the number of edges of the network.     

Transition Functions for Evolutionary Algorithms on Continuous State-space

Evolutionary algorithms working on continuous search space can be regarded as general homogeneous Markov chains. The finite space problem of describing the transition matrix turns into the more complicated problem of defining and estimating a transition function. We analyze in this respect the (1+1) evolutionary algorithm on the inclined plane and corridor models. In the first case, the probability of maximal success in n iterations is derived in closed form, under uniform mutation. For the second case, the algorithm is proved to escape the corner of the corridor for both uniform and normal mutation, exponentially fast.      

Heuristic Search Algorithms for the Minimum Volume Ellipsoid

Abstract

A method of robust estimation of multivariate location and shape that has attracted a lot of attention recently is Rousseeuw's minimum volume ellipsoid estimator (MVE). This estimator has a high breakdown point but is difficult to compute successfully. In this article, we apply methods of heuristic search to this problem, including simulated annealing, genetic algorithms, and tabu search, and compare the results to the undirected random search algorithm that is often cited. Heuristic search provides several effective algorithms that are far more computationally efficient than random search. Furthermore, random search, as currently implemented, is shown to be ineffective for larger problems.     

Using the WOWA operator in robust discrete optimization problems

In this paper a class of discrete optimization problems with uncertain costs is discussed. The uncertainty is modeled by introducing a scenario set containing a finite number of cost scenarios. A probability distribution over the set of scenarios is available. In order to choose a solution the weighted OWA criterion (WOWA) is applied. This criterion allows decision makers to take into account both probabilities for scenarios and the degree of pessimism/optimism. In this paper the complexity of the considered class of discrete optimization problems is described and some exact and approximation algorithms for solving it are proposed. Applications to the selection and the assignment problems, together with results of computational tests are shown.     

Efficient Parallel Algorithms for the Minimum Cost Flow Problem

In this paper, we propose efficient parallel implementations of the auction/sequential shortest path and the -relaxation algorithms for solving the linear minimum cost flow problem. In the parallel auction algorithm, several augmenting paths can be found simultaneously, each of them starting from a different node with positive surplus. Convergence results of an asynchronous version of the algorithm are also given. For the -relaxation method, there exist already parallel versions implemented on CM-5 and CM-2; our implementation is the first on a shared memory multiprocessor. We have obtained significant speedup values for the algorithms considered; it turns out that our implementations are effective and efficient.     

New Inexact Parallel Variable Distribution Algorithms

We consider the recently proposed parallel variable distribution(PVD) algorithm of Ferris and Mangasarian [4] for solvingoptimization problems in which the variables are distributed amongp processors. Each processor has the primary responsibility forupdating its block of variables while allowing the remainingsecondary variables tochange in a restricted fashion along some easily computable directions.We propose useful generalizationsthat consist, for the general unconstrained case, of replacing exact global solution ofthe subproblems by a certain natural sufficient descent condition, and,for the convex case, of inexact subproblem solution in thePVD algorithm. These modifications are the key features ofthe algorithm that has not been analyzed before.The proposed modified algorithms are more practical andmake it easier to achieve good load balancing among the parallelprocessors.We present a general framework for the analysis of thisclass of algorithms and derive some new and improved linear convergence resultsfor problems with weak sharp minima of order 2 and strongly convex problems.We also show that nonmonotone synchronization schemesare admissible, which further improves flexibility of PVD approach.     

Algorithms with Adaptive Smoothing for Finite Minimax Problems

We present a new feedback precision-adjustment rule for use with a smoothing technique and standard unconstrained minimization algorithms in the solution of finite minimax problems. Initially, the feedback rule keeps a precision parameter low, but allows it to grow as the number of iterations of the resulting algorithm goes to infinity. Consequently, the ill-conditioning usually associated with large precision parameters is considerably reduced, resulting in more efficient solution of finite minimax problems.The resulting algorithms are very simple to implement, and therefore are particularly suitable for use in situations where one cannot justify the investment of time needed to retrieve a specialized minimax code, install it on one's platform, learn how to use it, and convert data from other formats. Our numerical tests show that the algorithms are robust and quite effective, and that their performance is comparable to or better than that of other algorithms available in the Matlab environment.     

Fast and Flexible ADMM Algorithms for Trend Filtering

This article presents a fast and robust algorithm for trend filtering, a recently developed nonparametric regression tool. It has been shown that, for estimating functions whose derivatives are of bounded variation, trend filtering achieves the minimax optimal error rate, while other popular methods like smoothing splines and kernels do not. Standing in the way of a more widespread practical adoption, however, is a lack of scalable and numerically stable algorithms for fitting trend filtering estimates. This article presents a highly efficient, specialized alternating direction method of multipliers (ADMM) routine for trend filtering. Our algorithm is competitive with the specialized interior point methods that are currently in use, and yet is far more numerically robust. Furthermore, the proposed ADMM implementation is very simple, and, importantly, it is flexible enough to extend to many interesting related problems, such as sparse trend filtering and isotonic trend filtering. Software for our method is freely available, in both the C and R languages.     

Approximation Algorithms for Pick-and-Place Robots

In this paper we study the problem of finding placement tours for pick-and-place robots, also known as the printed circuit board assembly problem with m positions on a board, n bins containing m components and n locations for the bins. In the standard model where the working time of the robot is proportional to the distances travelled, the general problem appears as a combination of the travelling salesman problem and the matching problem, and for m=n we have an Euclidean, bipartite travelling salesman problem. We give a polynomial-time algorithm which achieves an approximation guarantee of 3+. An important special instance of the problem is the case of a fixed assignment of bins to bin-locations. This appears as a special case of a bipartite TSP satisfying the quadrangle inequality and given some fixed matching arcs. We obtain a 1.8 factor approximation with the stacker crane algorithm of Frederikson, Hecht and Kim. For the general bipartite case we also show a 2.0 factor approximation algorithm which is based on a new insertion technique for bipartite TSPs with quadrangle inequality. Implementations and experiments on real-world as well as random point configurations conclude this paper.     

线性约束优化问题的开关算法模型及其应用

本文利用开关函数．建立了解线性约束优化问题的一个组合型可行方向法─—开关算法模型，并给出了其收敛性质，从而统一、推广了包括起线性收敛的算法在内的常见的可行方向法．依此模型，具体构造了一类起线性收敛的新算法．