Rollout Algorithms for Stochastic Scheduling Problems

Stochastic scheduling problems are difficult stochastic control problems with combinatorial decision spaces. In this paper we focus on a class of stochastic scheduling problems, the quiz problem and its variations. We discuss the use of heuristics for their solution, and we propose rollout algorithms based on these heuristics which approximate the stochastic dynamic programming algorithm. We show how the rollout algorithms can be implemented efficiently, with considerable savings in computation over optimal algorithms. We delineate circumstances under which the rollout algorithms are guaranteed to perform better than the heuristics on which they are based. We also show computational results which suggest that the performance of the rollout policies is near-optimal, and is substantially better than the performance of their underlying heuristics.     

Hybrid Rollout Approaches for the Job Shop Scheduling Problem

In this paper, we focus on heuristic approaches for solving the deterministic job shop scheduling problem. More specifically, a new priority dispatch rule and hybrid rollout algorithms are developed for approaching the problem under consideration. The proposed solution algorithms are tested on a set of instances taken from the literature and compared with other methods. The computational results validate the effectiveness of the developed solution approaches and show that the proposed rollout algorithms are competitive with respect to several state-of-art heuristics for solving the job shop scheduling problem. The author thanks Dr. Marco Mancini and Dr. Alessandro Tarasio for valuable suggestions about computational issues.     

Efficient and Optimal Parallel Algorithms for Cholesky Decomposition

In this paper, we consider the problem of developing efficient and optimal parallel algorithms for Cholesky decomposition. We design our algorithms based on different data layouts and methods. We thereotically analyze the run-time of each algorithm. In order to determine the optimality of the algorithms designed, we derive theoretical lower bounds on running time based on initial data layout and compare them against the algorithmic run-times. To address portability, we design our algorithms and perform complexity analysis on the LogP model. Lastly, we implement our algorithms and analyze performance data. This revised version was published online in July 2006 with corrections to the Cover Date.     

Speedup estimates for some variants of the parallel implementations of the branch-and-bound method

The efficiency of parallel implementations of the branch-and-bound method in discrete optimization problems is considered. A theoretical analysis and comparison of two parallel implementations of this method is performed. A mathematical model of the computation process is constructed and used to obtain estimates of the maximum possible speedup. Examples of problems in which none of these two parallel implementations can speed up the computations are considered.     

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.     

Parallel Synchronous and Asynchronous Space-Decomposition Algorithms for Large-Scale Minimization Problems

Three parallel space-decomposition minimization (PSDM) algorithms, based on the parallel variable transformation (PVT) and the parallel gradient distribution (PGD) algorithms (O.L. Mangasarian, SIMA Journal on Control and Optimization, vol. 33, no. 6, pp. 1916–1925.), are presented for solving convex or nonconvex unconstrained minimization problems. The PSDM algorithms decompose the variable space into subspaces and distribute these decomposed subproblems among parallel processors. It is shown that if all decomposed subproblems are uncoupled of each other, they can be solved independently. Otherwise, the parallel algorithms presented in this paper can be used. Numerical experiments show that these parallel algorithms can save processor time, particularly for medium and large-scale problems. Up to six parallel processors are connected by Ethernet networks to solve four large-scale minimization problems. The results are compared with those obtained by using sequential algorithms run on a single processor. An application of the PSDM algorithms to the training of multilayer Adaptive Linear Neurons (Madaline) and a new parallel architecture for such parallel training are also presented.     

Parallel Algorithms for Hierarchical Clustering and Applications to Split Decomposition and Parity Graph Recognition

We present efficient (parallel) algorithms for two hierarchical clustering heuristics. We point out that these heuristics can also be applied to solving some algorithmic problems in graphs, including split decomposition. We show that efficient parallel split decomposition induces an efficient parallel parity graph recognition algorithm. This is a consequence of the result of S. Cicerone and D. Di Stefano [[7]] that parity graphs are exactly those graphs that can be split decomposed into cliques and bipartite graphs.     

Analysis of a Rollout Approach to Sequencing Problems with Stochastic Routing Applications

The paper considers sequencing problems, the traveling salesman problem being their natural representative. It studies a rollout approach that employs a cyclic heuristic as its main base algorithm. The theoretical analysis establishes that it is guaranteed to improve (at least in a weak sense) the quality of any feasible solution to a given sequencing problem. Besides other applications, the paper shows that it is well suited for applications that are embedded in dynamic and stochastic environments. The computational performance of the approach is investigated with applications to two stochastic routing problems. The dynamic version of the heuristic appears to be the first algorithm available in the literature to approximately solve a variant of one of these problems.     

Parallel Algorithm for Unconstrained Optimization Based on Decomposition Techniques

We present a numerical implementation of the parallel gradient distribution (PGD) method for the solution of large-scale unconstrained optimization problems. The proposed parallel algorithm is characterized by a parallel phase which exploits the portions of the gradient of the objective function assigned to each processor; then, a coordination phase follows which, by a synchronous interaction scheme, optimizes over the partial results obtained by the parallel phase. The parallel and coordination phases are implemented using a quasi-Newton limited-memory BFGS approach. The computational experiments, carried out on a network of UNIX workstations by using the parallel software tool PVM, show that parallelization efficiency was problem dependent and ranged between 0.15 and 8.75. For the 150 problems solved by PGD on more than one processor, 85 cases had parallelization efficiency below 1, while 65 cases had a parallelization efficiency above 1.     

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.     

Parallel Algorithms for Solving the Convex Minimum Cost Flow Problem

In this paper we deal with the solution of the separable convex cost network flow problem. In particular, we propose a parallel asynchronous version of the -relaxation method and we prove theoretically its correctness.We present two implementations of the parallel method for a shared memory multiprocessor system, and we empirically analyze their numerical performance on different test problems. The preliminary numerical results show a good reduction of the execution time of the parallel algorithm with the respect to the sequential counterpart.     

Approximation Algorithms for the Achromatic Number

The achromatic number for a graph G = V, E is the largest integer m such that there is a partition of V into disjoint independent sets {V₁, …, V_m} such that for each pair of distinct sets V_i, V_j, V_i V_j is not an independent set in G. Yannakakis and Gavril (1980, SIAM J. Appl. Math.38, 364–372) proved that determining this value for general graphs is NP-complete. For n-vertex graphs we present the first o(n) approximation algorithm for this problem. We also present an O(n^5/12) approximation algorithm for graphs with girth at least 5 and a constant approximation algorithm for trees.     

Approximation Algorithms for Dispersion Problems

Given a collection of weighted sets, each containing at most k elements drawn from a finite base set, the k-set packing problem is to find a maximum weight sub-collection of disjoint sets. A greedy algorithm for this problem approximates it to within a factor of k, and a natural local search has been shown to approximate it to within a factor of roughly k − 1. However, neither paradigm can yield approximations that improve on this.We present an approximation algorithm for the weighted k-set packing problem that combines the two paradigms by starting with an initial greedy solution and then repeatedly choosing the best possible local improvement. The algorithm has a performance ratio of 2(k + 1)/3, which we show is asymptotically tight. This is the first asymptotic improvement over the straightforward ratio of k.     

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．     

Parallel Asynchronous Algorithms for the K Shortest Paths Problem

We deal with the problem of computing in parallel the first K shortest paths from a single origin node to all other nodes of a directed graph. Our approach is based on a very easy way to introduce parallelism in the typical sequential label-correcting method. We describe formally an asynchronous method defined on a shared-memory parallel computational model, and we prove its theoretical correctness under very weak assumptions. According to different strategies used to implement the generic method, we propose several parallel label-correcting algorithms, and we analyze the numerical performance of the resulting codes on a nonuniform memory access multiprocessor via computational results collected on random generated networks. The advantage of parallelism is significant for very large-scale problems of practical interest.     

A parallel method for finding the global minimum of univariate functions

We describe a new parallel method for solving global optimization problems. The formulation of the decision rules of this method is presented. We examine convergence conditions of the proposed algorithm and establish conditions which guarantee a considerable speedup with respect to the sequential version of the algorithm. We also present some numerical experiments executed on Alliant FX/80 for one class of multiextremal functions.The authors are greatly indebted to R. G. Strongin who stimulated the fulfillment of this research. They also would like to thank the anonymous referees for their useful suggestions.The research of the first author was partially supported by Grant 9494/NC/89 from the Italian Government under the Italian-Soviet Agreement about the Cultural and Scientific Exchange in 1990–1991. He thanks the Systems Department, University of Calabria, where he was a Visitor.     

Parallelization alternatives and their performance for the convex hull problem

High performance machines have become available nowadays to an increasing number of researchers. Most of us might have both an access to a supercomputing center and an algorithm that could benefit from these high performance machines. The aim of the present work is to revisit all existing parallelization alternatives, including emerging technologies like software-only speculative parallelization, to solve on different architectures the same representative problem: The computation of the convex hull of a point set.     

最优化问题的并行算法

本文对求解非线性最优化问题的几种主要并行思想，即按变量分裂的并行算法，函数值、梯度值的并行计算，计算步骤并行的算法等，作了简要的综述，并介绍了近几年在这方面取得的进展．     

Superlinear Convergence of Interior-Point Algorithms for Semidefinite Programming

We prove the superlinear convergence of the primal-dual infeasible interior-point path-following algorithm proposed recently by Kojima, Shida, and Shindoh and by the present authors, under two conditions: (i) the semidefinite programming problem has a strictly complementary solution; (ii) the size of the central path neighborhood approaches zero. The nondegeneracy condition suggested by Kojima, Shida, and Shindoh is not used in our analysis. Our result implies that the modified algorithm of Kojima, Shida, and Shindoh, which enforces condition (ii) by using additional corrector steps, has superlinear convergence under the standard assumption of strict complementarity. Finally, we point out that condition (ii) can be made weaker and show the superlinear convergence under the strict complementarity assumption and a weaker condition than (ii).