Approximative procedures for no-wait job shop scheduling

In this article we consider the no-wait job shop problem with makespan objective. Based on a decomposition of the problem into a sequencing and a timetabling problem, we propose two local search algorithms. Extensive computational tests in which the algorithms compare favorably to the best existing strategies are reported. Although not specifically designed for that purpose, our algorithms also outperform one of the best no-wait flow shop algorithms in literature.     

Several Variants of the Primal-Dual Hybrid Gradient Algorithm with Applications

By reviewing the primal-dual hybrid gradient algorithm (PDHG) proposed by He, You and Yuan (SIAM J. Image Sci., 7(4) (2014), pp. 2526-2537), in this paper we introduce four improved schemes for solving a class of saddle-point problems. Convergence properties of the proposed algorithms are ensured based on weak assumptions, where none of the objective functions are assumed to be strongly convex but the step-sizes in the primal-dual updates are more flexible than the previous. By making use of variational analysis, the global convergence and sublinear convergence rate in the ergodic/nonergodic sense are established, and the numerical efficiency of our algorithms is verified by testing an image deblurring problem compared with several existing algorithms.     

Solving dual problems using a coevolutionary optimization algorithm

In solving certain optimization problems, the corresponding Lagrangian dual problem is often solved simply because in these problems the dual problem is easier to solve than the original primal problem. Another reason for their solution is the implication of the weak duality theorem which suggests that under certain conditions the optimal dual function value is smaller than or equal to the optimal primal objective value. The dual problem is a special case of a bilevel programming problem involving Lagrange multipliers as upper-level variables and decision variables as lower-level variables. Another interesting aspect of dual problems is that both lower and upper-level optimization problems involve only box constraints and no other equality of inequality constraints. In this paper, we propose a coevolutionary dual optimization (CEDO) algorithm for co-evolving two populations—one involving Lagrange multipliers and other involving decision variables—to find the dual solution. On 11 test problems taken from the optimization literature, we demonstrate the efficacy of CEDO algorithm by comparing it with a couple of nested smooth and nonsmooth algorithms and a couple of previously suggested coevolutionary algorithms. The performance of CEDO algorithm is also compared with two classical methods involving nonsmooth (bundle) optimization methods. As a by-product, we analyze the test problems to find their associated duality gap and classify them into three categories having zero, finite or infinite duality gaps. The development of a coevolutionary approach, revealing the presence or absence of duality gap in a number of commonly-used test problems, and efficacy of the proposed coevolutionary algorithm compared to usual nested smooth and nonsmooth algorithms and other existing coevolutionary approaches remain as the hallmark of the current study.     

Variable dimension algorithms: Basic theory,interpretations and extensions of some existing methods

In this paper we establish a basic theory for variable dimension algorithms which were originally developed for computing fixed points by Van der Laan and Talman. We introduce a new concept primal—dual pair of subdivided manifolds and by utilizing it we propose a basic model which will serve as a foundation for constructing a wide class of variable dimension algorithms. Our basic model furnishes interpretations to several existing methods: Lemke's algorithm for the linear complementarity problem, its extension to the nonlinear complementarity problem, a variable dimension algorithm on conical subdivisions and Merrill's algorithm. We shall present a method for solving systems of equations as an application of the second method and an efficient implementation of the fourth method to which our interpretation leads us. A method for constructing triangulations with an arbitrary refinement factor of mesh size is also proposed.     

Decomposition methods for a spatial model for long-term energy pricing problem

We consider an energy production network with zones of production and transfer links. Each zone representing an energy market (a country, part of a country or a set of countries) has to satisfy the local demand using its hydro and thermal units and possibly importing and exporting using links connecting the zones. Assuming that we have the appropriate tools to solve a single zonal problem (approximate dynamic programming, dual dynamic programming, etc.), the proposed algorithm allows us to coordinate the productions of all zones. We propose two reformulations of the dynamic model which lead to different decomposition strategies. Both algorithms are adaptations of known monotone operator splitting methods, namely the alternating direction method of multipliers and the proximal decomposition algorithm which have been proved to be useful to solve convex separable optimization problems. Both algorithms present similar performance in theory but our numerical experimentation on real-size dynamic models have shown that proximal decomposition is better suited to the coordination of the zonal subproblems, becoming a natural choice to solve the dynamic optimization of the European electricity market.     

Incremental-like bundle methods with application to energy planning

An important field of application of non-smooth optimization refers to decomposition of large-scale or complex problems by Lagrangian duality. In this setting, the dual problem consists in maximizing a concave non-smooth function that is defined as the sum of sub-functions. The evaluation of each sub-function requires solving a specific optimization sub-problem, with specific computational complexity. Typically, some sub-functions are hard to evaluate, while others are practically straightforward. When applying a bundle method to maximize this type of dual functions, the computational burden of solving sub-problems is preponderant in the whole iterative process. We propose to take full advantage of such separable structure by making a dual bundle iteration after having evaluated only a subset of the dual sub-functions, instead of all of them. This type of incremental approach has already been applied for subgradient algorithms. In this work we use instead a specialized variant of bundle methods and show that such an approach is related to bundle methods with inexact linearizations. We analyze the convergence properties of two incremental-like bundle methods. We apply the incremental approach to a generation planning problem over an horizon of one to three years. This is a large scale stochastic program, unsolvable by a direct frontal approach. For a real-life application on the French power mix, we obtain encouraging numerical results, achieving a significant improvement in speed without losing accuracy.     

FAST PARALLELIZABLE METHODS FOR COMPUTING INVARIANT SUBSPACES OF HERMITIAN MATRICES

We propose a quadratically convergent algorithm for computing the invariant subspaces of an Hermitian matrix. Each iteration of the algorithm consists of one matrix-matrix multiplication and one QR decomposition. We present an accurate convergence analysis of the algorithm without using the big O notation. We also propose a general framework based on implicit rational transformations which allows us to make connections with several existing algorithms and to derive classes of extensions to our basic algorithm with faster convergence rates. Several numerical examples are given which compare some aspects of the existing algorithms and the new algorithms.     

求解张量随机互补问题的光滑牛顿算法

近年来, 越来越多的人意识到随机互补问题在经济管理中具有十分重要的作用。有学者已将随机互补问题由矩阵推广到张量, 并提出了张量随机互补问题。本文通过引入一类光滑函数, 提出了求解张量随机互补问题的一种光滑牛顿算法, 并证明了算法的全局和局部收敛性, 最后通过数值实验验证了算法的有效性。     

A Dual Algorithm for Minimizing a Quadratic Function with Two Quadratic Constraints

In this paper, we present a dual algorithm for minimizing a convex quadratic function with two quadratic constraints. Such a minimization problem is a subproblem that appears in some trust region algorithms for general nonlinear programming. Some theoretical properties of the dual problem are given. Global convergence of the algorithm is proved and a local superlinear convergence result is presented. Numerical examples are also provided.     

Primal and dual predicted decrease approximation methods

We introduce the notion of predicted decrease approximation (PDA) for constrained convex optimization, a flexible framework which includes as special cases known algorithms such as generalized conditional gradient, proximal gradient, greedy coordinate descent for separable constraints and working set methods for linear equality constraints with bounds. The new scheme allows the development of a unified convergence analysis for these methods. We further consider a partially strongly convex nonsmooth model and show that dual application of PDA-based methods yields new sublinear convergence rate estimates in terms of both primal and dual objectives. As an example of an application, we provide an explicit working set selection rule for SMO-type methods for training the support vector machine with an improved primal convergence analysis.     

一类非线性单调型问题的平行化算法

一类非线性单调型问题的平行化算法许学军，蒋美群（苏州大学数学系）ＭＲＡＬＬＥＬＡＬＧＯＲＩＴＨＭＳＦＯＲＡＮＯＮＬＩＮＥＡＲＭＯＮＯＴＯＮＥＰＲＯＢＬＥＭ￥ＸｕＸｕｅ－ｊｕｎ；ＪｉａｎｇＭｅｉ－ｑｕｎ（ＳｕｚｈｏｕＵｎｉｖｅｒｓｉｔｙ，Ｓｕｚｈｏｕ）...     

The Gauss-Newton Methods via Conjugate Gradient Path without Line Search Technique for Solving Nonlinear Systems

In this article, we propose the Gauss-Newton methods via conjugate gradient path for solving nonlinear systems. By constructing and solving a linearized model of the nonlinear systems, we obtain the iterative direction by employing the conjugate gradient path. In successive iterations, the approximate Jacobian of the nonlinear systems is updated by a Broyden formula to construct the conjugate path. The global convergence and local superlinear convergence rate of the proposed algorithms are established under some reasonable conditions. Finally, the numerical results are reported to show the effectiveness of the proposed algorithms.     

Matrix computations for information retrieval and major and outlier cluster detection

In this paper we introduce COV, a novel information retrieval (IR) algorithm for massive databases based on vector space modeling and spectral analysis of the covariance matrix, for the document vectors, to reduce the scale of the problem. Since the dimension of the covariance matrix depends on the attribute space and is independent of the number of documents, COV can be applied to databases that are too massive for methods based on the singular value decomposition of the document-attribute matrix, such as latent semantic indexing (LSI). In addition to improved scalability, theoretical considerations indicate that results from our algorithm tend to be more accurate than those from LSI, particularly in detecting subtle differences in document vectors. We demonstrate the power and accuracy of COV through an important topic in data mining, known as outlier cluster detection. We propose two new algorithms for detecting major and outlier clusters in databases—the first is based on LSI, and the second on COV. Our implementation studies indicate that our cluster detection algorithms outperform the basic LSI and COV algorithm in detecting outlier clusters.     

求解张量互补问题的一类光滑模系矩阵迭代方法

本文提出了求解张量互补问题的一类光滑模系矩阵迭代方法.其基本思想是,先将张量互补问题转化为等价的模系方程组,然后引入一个逼近的光滑函数进行求解.我们分析了算法的收敛性,并通过数值实验验证了所提出算法的有效性.     

SCHWARZ DOMAIN DECOMPOSITION ALGORITHMS FOR BOUNDARY ELEMENT METHOD

In this paper, we introduce two Schwarz type domain decomposition algorithms for solving boundary element equations, which decompose the original problem defined on global boundary surface into several ones defined on sub-domains so that they may be solved ileratively or parallelly. The convergence of these methods are also proved.     

面向绿色智能制造的高维多目标动态作业车间调度优化

在当前环境问题日益严峻情况下,绿色智能制造受到广泛关注。在动态柔性作业车间基础上考虑不同机器状态下的能耗情况、机器使用节能方法,构建以极小化总能耗、最大完工时间、机器总负荷和产品质量稳定性为目标的高维多目标绿色动态柔性作业车间调度模型,并设计改进的灰狼优化IMOGWO算法求解该问题。首先,采用反向学习初始化种群策略,以扩大种群多样性;然后,依据多目标问题和标准GWO算法的特点提出多级官员领导机制,并引入POX交叉和逆序变异算子;最后,改进精英保留策略用于多目标优化算法。为证明算法的有效性,设计两组仿真实验分别对三种算法进行比较。实验结果表明,运用本文改进的IMOGWO算法求解多目标问题有更好的收敛性和分布性。     

Rate of Convergence for some constraint decomposition methods for nonlinear variational inequalities

Summary. Some general subspace correction algorithms are proposed for a convex optimization problem over a convex constraint subset. One of the nontrivial applications of the algorithms is the solving of some obstacle problems by multilevel domain decomposition and multigrid methods. For domain decomposition and multigrid methods, the rate of convergence for the algorithms for obstacle problems is of the same order as the rate of convergence for jump coefficient linear elliptic problems. In order to analyse the convergence rate, we need to decompose a finite element function into a sum of functions from the subspaces and also satisfying some constraints. A special nonlinear interpolation operator is introduced for decomposing the functions. Received December 13, 2001 / Revised version received February 19, 2002 / Published online June 17, 2002 This work was partially supported by the Norwegian Research Council under projects 128224/431 and SEP-115837/431.     

On the Best Least Squares Approximation of Continuous Functions using Linear Splines with Free Knots

Approximations to continuous functions by linear splines cangenerally be greatly improved if the knot points are free variables.In this paper we address the problem of computing a best linearspline L₂-approximant to a given continuous function on a givenclosed real interval with a fixed number of free knots. We describe an algorithm that is currently available and establishthe theoretical basis for two new algorithms that we have developedand tested. We show that one of these new algorithms had goodlocal convergence properties by comparison with the other techniques,though its convergence is quite slow. The second new algorithmis not so robust but is quicker and so is used to aid efficiency.A starting procedure based on a dynamic programming approachis introduced to give more reliable global convergence properties. We thus propose a hybrid algorithm which is both robust andreasonably efficient for this problem.     

Subspace-stabilized sequential quadratic programming

The stabilized sequential quadratic programming (SQP) method has nice local convergence properties: it possesses local superlinear convergence under very mild assumptions not including any constraint qualifications. However, any attempts to globalize convergence of this method indispensably face some principal difficulties concerned with intrinsic deficiencies of the steps produced by it when relatively far from solutions; specifically, it has a tendency to produce long sequences of short steps before entering the region where its superlinear convergence shows up. In this paper, we propose a modification of the stabilized SQP method, possessing better “semi-local” behavior, and hence, more suitable for the development of practical realizations. The key features of the new method are identification of the so-called degeneracy subspace and dual stabilization along this subspace only; thus the name “subspace-stabilized SQP”. We consider two versions of this method, their local convergence properties, as well as a practical procedure for approximation of the degeneracy subspace. Even though we do not consider here any specific algorithms with theoretically justified global convergence properties, subspace-stabilized SQP can be a relevant substitute for the stabilized SQP in such algorithms using the latter at the “local phase”. Some numerical results demonstrate that stabilization along the degeneracy subspace is indeed crucially important for success of dual stabilization methods.     

Algorithmic aspects of the substitution decomposition in optimization over relations,set systems and Boolean functions

In the last years, decomposition techniques have seen an increasing application to the solution of problems from operations research and combinatorial optimization, in particular in network theory and graph theory. This paper gives a broad treatment of a particular aspect of this approach, viz. the design of algorithms to compute the decomposition possibilities for a large class of discrete structures. The decomposition considered is thesubstitution decomposition (also known as modular decomposition, disjunctive decomposition, X-join or ordinal sum). Under rather general assumptions on the type of structure considered, these (possibly exponentially many) decomposition possibilities can be appropriately represented in acomposition tree of polynomial size. The task of determining this tree is shown to be polynomially equivalent to the seemingly weaker task of determining the closed hull of a given set w.r.t. a closure operation associated with the substitution decomposition. Based on this reduction, we show that for arbitrary relations the composition tree can be constructed in polynomial time. For clutters and monotonic Boolean functions, this task of constructing the closed hull is shown to be Turing-reducible to the problem of determining the circuits of the independence system associated with the clutter or the prime implicants of the Boolean function. This leads to polynomial algorithms for special clutters or monotonic Boolean functions. However, these results seem not to be extendable to the general case, as we derive exponential lower bounds for oracle decomposition algorithms for arbitrary set systems and Boolean functions.