Lagrangian decomposition of block-separable mixed-integer all-quadratic programs

The purpose of this paper is threefold. First we propose splitting schemes for reformulating non-separable problems as block-separable problems. Second we show that the Lagrangian dual of a block-separable mixed-integer all-quadratic program (MIQQP) can be formulated as an eigenvalue optimization problem keeping the block-separable structure. Finally we report numerical results on solving the eigenvalue optimization problem by a proximal bundle algorithm applying Lagrangian decomposition. The results indicate that appropriate block-separable reformulations of MIQQPs could accelerate the running time of dual solution algorithms considerably.The work was supported by the German Research Foundation (DFG) under grant NO 421/2-1Mathematics Subject Classification (2000): 90C22, 90C20, 90C27, 90C26, 90C59     

解非线性单调方程组的投影自调比对称秩1拟牛顿法

本文给出了求解非线性单调方程组的两个自调比对称秩1牛顿法,即投影SSR1法和投影有限储存SSR1法.这两个算法将自调比对称秩1校正参数进行了一个简单的修改并采用了保守策略.在非线性单调函数满足李普希茨连续的条件下,证明了算法的全局收敛性,并与相同类型的BFGS法进行了初步的数值比较试验,试验结果表明自调比对称秩1类投影算法求解非线性单调方程组与相同类型的BFGS数值结果相当.     

Dealing with inconsistent judgments in multiple criteria sorting models

Sorting models consist in assigning alternatives evaluated on several criteria to ordered categories. To implement such models it is necessary to set the values of the preference parameters used in the model. Rather than fixing the values of these parameters directly, a usual approach is to infer these values from assignment examples provided by the decision maker (DM), i.e., alternatives for which (s)he specifies a required category. However, assignment examples provided by DMs can be inconsistent, i.e., may not match the sorting model. In such situations, it is necessary to support the DMs in the resolution of this inconsistency. In this paper, we extend algorithms from mous5ejor03 that calculate different ways to remove assignment examples so that the information can be represented in the sorting model. The extension concerns the possibility to relax (rather than to delete) assignment examples. These algorithms incorporate information about the confidence attached to each assignment example, hence providing inconsistency resolutions that the DMs are most likely to accept. Received: September 2004, Revised: June 2005 AMS classification: 90B50, 91B08, 90C05     

Duality methods with an automatic choice of parameters Application to shallow water equations in conservative form

Summary.   In [3] a duality numerical algorithm for solving variational inequalities based on certain properties of the Yosida approximation of maximal monotone operators has been introduced. The performance of this algorithm strongly depends on the choice of two constant parameters. In this paper, we consider a new class of algorithms where these constant parameters are replaced by functions. We show that convergence properties are preserved and look for optimal values of these two functions. In general these optimal values cannot be computed, as they depend on the exact solution. Therefore, we propose some strategies in order to approximate them. The resulting algorithms are applied to three variational inequalities in order to compare their performance with that of the original algorithm. Received July 20, 1998 / Revised version received November 26, 1999 / Published online February 5, 2001     

Two-grid stabilized algorithms for the steady Navier–Stokes equations with damping

This paper presents and studies three two-grid stabilized quadratic equal-order finite element algorithms based on two local Gauss integrations for the steady Navier–Stokes equations with damping. In these algorithms, we first solve a stabilized nonlinear problem on a coarse grid, and then pass the coarse grid solution to a fine grid and solve a stabilized linear problem. Using some nonlinear analysis techniques, we analyze stability of the algorithms and derive optimal order error estimates of the approximate solutions. Theoretical and numerical results show that, when the algorithmic parameters are chosen appropriately, the accuracy of the approximate solutions computed by our two-grid stabilized algorithms is comparable to that of solving a fully stabilized nonlinear problem on the same fine grid; however, our two-grid algorithms save a large amount of CPU time than the one-grid stabilized algorithm.     

Parallel branch, cut, and price for large-scale discrete optimization

In discrete optimization, most exact solution approaches are based on branch and bound, which is conceptually easy to parallelize in its simplest forms. More sophisticated variants, such as the so-called branch, cut, and price algorithms, are more difficult to parallelize because of the need to share large amounts of knowledge discovered during the search process. In the first part of the paper, we survey the issues involved in parallelizing such algorithms. We then review the implementation of SYMPHONY and COIN/BCP, two existing frameworks for implementing parallel branch, cut, and price. These frameworks have limited scalability, but are effective on small numbers of processors. Finally, we briefly describe our next-generation framework, which improves scalability and further abstracts many of the notions inherent in parallel BCP, making it possible to implement and parallelize more general classes of algorithms. Mathematics Subject Classification (1991):65K05, 68N99, 68W10, 90-04, 90-08, 90C06, 90C09, 90C10, 90C11, 90C57     

Models and algorithms for a staff scheduling problem

We present mathematical models and solution algorithms for a family of staff scheduling problems arising in real life applications. In these problems, the daily assignments to be performed are given and the durations (in days) of the working and rest periods for each employee in the planning horizon are specified in advance, whereas the sequence in which these working and rest periods occur, as well as the daily assignment for each working period, have to be determined. The main objective is the minimization of the number of employees needed to perform all daily assignments in the horizon. We decompose the problem into two steps: the definition of the sequence of working and rest periods (called pattern) for each employee, and the definition of the daily assignment to be performed in each working period by each employee. The first step is formulated as a covering problem for which we present alternative ILP models and exact enumerative algorithms based on these models. Practical experience shows that the best approach is based on the model in which variables are associated with feasible patterns and generated either by dynamic programming or by solving another ILP. The second step is stated as a feasibility problem solved heuristically through a sequence of transportation problems. Although in general this procedure may not find a solution (even if one exists), we present sufficient conditions under which our approach is guaranteed to succeed. We also propose an iterative heuristic algorithm to handle the case in which no feasible solution is found in the second step. We present computational results on real life instances associated with an emergency call center. The proposed approach is able to determine the optimal solution of instances involving up to several hundred employees and a working period of up to 6 months. Mathematics Subject Classification (2000): 90B70, 90C10, 90C27, 90C39, 90C57, 90C59     

Shadow block iteration for solving linear systems obtained from wavelet transforms

Matrices resulting from wavelet transforms have a special “shadow” block structure, that is, their small upper left blocks contain their lower frequency information. Numerical solutions of linear systems with such matrices require special care. We propose shadow block iterative methods for solving linear systems of this type. Convergence analysis for these algorithms are presented. We apply the algorithms to three applications: linear systems arising in the classical regularization with a single parameter for the signal de-blurring problem, multilevel regularization with multiple parameters for the same problem and the Galerkin method of solving differential equations. We also demonstrate the efficiency of these algorithms by numerical examples in these applications.     

Numerical evaluation of SBmethod

 We report numerical results for SBmethod – a publically available implementation of the spectral bundle method – applied to the 7$^{th}$ DIMACS challenge test sets that are semidefinite relaxations of combinatorial optimization problems. The performance of the code is heavily influenced by parameters that control bundle update and eigenvalue computation. Unfortunately, no mathematically sound guidelines for setting them are known. Based on our experience with SBmethod, we propose heuristics for dynamically updating the parameters as well as a heuristc for improving the starting point. These are now the default settings of SBmethod Version 1.1. We compare their performance on the DIMACS instances to our previous best choices for Version 1.0. SBmethod Version 1.1 is also part of the independent DIMACS benchmark by H. Mittelmann. Based on these results we try to analyze strengths and weaknesses of our approach in comparison to other codes for large scale semidefinite programming. Received: June 21, 2001 / Accepted: April 19, 2002 Published online: September 27, 2002 Mathematics Subject Classification (2000): 90C22, 90C06     

Rank-k modification methods for recursive least squares problems

In least squares problems, it is often desired to solve the same problem repeatedly but with several rows of the data either added, deleted, or both. Methods for quickly solving a problem after adding or deleting one row of data at a time are known. In this paper we introduce fundamental rank-k updating and downdating methods and show how extensions of rank-1 downdating methods based on LINPACK, Corrected Semi-Normal Equations (CSNE), and Gram-Schmidt factorizations, as well as new rank-k downdating methods, can all be derived from these fundamental results. We then analyze the cost of each new algorithm and make comparisons tok applications of the corresponding rank-1 algorithms. We provide experimental results comparing the numerical accuracy of the various algorithms, paying particular attention to the downdating methods, due to their potential numerical difficulties for ill-conditioned problems. We then discuss the computation involved for each downdating method, measured in terms of operation counts and BLAS calls. Finally, we provide serial execution timing results for these algorithms, noting preferable points for improvement and optimization. From our experiments we conclude that the Gram-Schmidt methods perform best in terms of numerical accuracy, but may be too costly for serial execution for large problems.Research supported in part by the Joint Services Electronics Program, contract no. F49620-90-C-0039.     

解不适定问题的快速多层方法

Many industrial and engineering applications require numerically solving ill-posed problems. Regularization methods are employed to find approximate solutions of these problems. The choice of regularization parameters by numerical algorithms is one of the most important issues for the success of regularization methods. When we use some discrepancy principles to determine the regularization parameter,     

Relaxation methods for solving the tensor equation arising from the higher‐order Markov chains

In this paper, we propose several relaxation algorithms for solving the tensor equation arising from the higher‐order Markov chain and the multilinear PageRank. The semi‐symmetrization technique on the original equation is also employed to modify the proposed algorithms. The convergence analysis is given for the proposed algorithms. It is shown that the new algorithms are more efficient than the existing ones by some numerical experiments when relaxation parameters are chosen suitably.     

Numerical investigation of bifurcations of equilibria and Hopf bifurcations in disease transmission models

One of the general SIRS disease transmission model is considered under the assumptions that the size of the population varies, the incidence rate is nonlinear, and the recovered (removed) class may also be directly reinfected. A combination of analytical and numerical techniques is used to show that (for some parameters) the bifurcations of equilibria can occur and also asymptotically orbitally stable periodic solutions with asymptotic phase can arise through Hopf bifurcations. The investigation is based on computer simulation of bifurcation manifolds in the parameter space. Hopf bifurcations are investigated on the base of center manifold theory by the computation of bifurcation parameters and the approximation of Hopf-bifurcating cycles by bifurcation formulas. This method finds the limit cycle to a good approximation and also its stability. For computer simulations the necessary computer oriented algorithms were developed and encoded by C++. Some results of computer simulations are presented and numerical evidence of existence of bifurcations of equilibria and Hopf bifurcations for the considered model is provided.     

On one approach to finding the branching lines and bifurcation points of solutions of nonlinear integral equations whose kernels depend analytically on two spectral parameters

We consider iterative algorithms of finding the curves of eigenvalues and their bifurcation points of a nonlinear algebraic two-parameter spectral problem, which appears in the solution of the problem of synthesis of plane antenna arrays by a given amplitude directivity pattern. These algorithms are based on the numerical procedure of calculation of ordinary and partial derivatives of a matrix determinant and the algorithm of finding all eigenvalues in a given domain of change in the spectral parameters. We also present some results of numerical experiments.     

From polynomial matrices to markov parameters and back: Theory and numerical algorithms

We consider polynomial matrix representations of MIMO linear systems and their connection to Markov parameters. Specifically, we consider polynomial matrix models in an arbitrary operator ρ, and develop theory and numerical algorithms for transforming polynomial matrix models into Markov parameter models, and vice versa. We also provide numerical examples to illustrate the proposed algorithms.     

Approximation algorithms for nonnegative polynomial optimization problems over unit spheres

We consider approximation algorithms for nonnegative polynomial optimization problems over unit spheres. These optimization problems have wide applications e.g., in signal and image processing, high order statistics, and computer vision. Since these problems are NP-hard, we are interested in studying on approximation algorithms. In particular, we propose some polynomial-time approximation algorithms with new approximation bounds. In addition, based on these approximation algorithms, some efficient algorithms are presented and numerical results are reported to show the efficiency of our proposed algorithms.     

Grid Methods in Computational Real Algebraic (and Semialgebraic) Geometry

In recent years, a family of numerical algorithms to solve problems in real algebraic and semialgebraic geometry has been slowly growing. Unlike their counterparts in symbolic computation they are numerically stable. But their complexity analysis, based on the condition of the data, is radically different from the usual complexity analysis in symbolic computation as these numerical algorithms may run forever on a thin set of ill-posed inputs.     

奇异H-矩阵并行算法

1　引　　言对于H矩阵类,到目前为止,人们关注的是非奇异H矩阵,对于奇异H矩阵研究结果很少,不象奇异M-矩阵研究的丰富[1-4]及获得了半收敛的一些结论,王川龙和游兆永将并行算法用于奇异M矩阵[5].本文的目的就是将并行算法用于奇异H矩阵.为此,首先讨论了奇异H矩阵与奇异M矩阵的关系.2　符号特征设Mn(R)代表实方阵的全体,A∈Mn(R),不特殊说明,A=D-B表示Jacobi分裂,〈A〉是A的比较矩阵,detA表示A的行列式,ρ(A)表示A的谱半径,μ(A)表示A的谱〈n〉={1,2,…,n},A[α|α]表示由α所决定的主子矩阵,α∈〈n〉.定理2.1[8]　设A是实H矩阵…     

Recent advances in the solution of quadratic assignment problems

 The quadratic assignment problem (QAP) is notoriously difficult for exact solution methods. In the past few years a number of long-open QAPs, including those posed by Steinberg (1961), Nugent et al. (1968) and Krarup (1972) were solved to optimality for the first time. The solution of these problems has utilized both new algorithms and novel computing structures. We describe these developments, as well as recent work which is likely to result in the solution of even more difficult instances. Received: February 13, 2003 / Accepted: April 22, 2003 Published online: May 28, 2003 Key Words. quadratic assignment problem – discrete optimization – branch and bound Mathematics Subject Classification (1991): 90C27, 90C09, 90C20     

Convergence and stability of two-level penalty mixed finite element method for stationary Navier-Stokes equations

The two-level penalty mixed finite element method for the stationary Navier-Stokes equations based on Taylor-Hood element is considered in this paper. Two algorithms are proposed and analyzed. Moreover, the optimal stability analysis and error estimate for these two algorithms are provided. Finally, the numerical tests confirm the theoretical results of the presented algorithms.