An Efficient Algorithm for the Knapsack Sharing Problem

The Knapsack Sharing Problem (KSP) is an NP-Hard combinatorial optimization problem, admitted in numerous real world applications. In the KSP, we have a knapsack of capacity c and a set of n objects, namely N, where each object j, j = 1,...,n, is associated with a profit p _j and a weight w _j. The set of objects N is composed of m different classes of objects J _i, i = 1,...,m, and N = ^m _i=1 J _i. The aim is to determine a subset of objects to be included in the knapsack that realizes a max-min value over all classes.In this article, we solve the KSP using an approximate solution method based upon tabu search. First, we describe a simple local search in which a depthparameter and a tabu list are used. Next, we enhance the algorithm by introducing some intensifying and diversifying strategies. The two versions of the algorithm yield satisfactory results within reasonable computational time. Extensive computational testing on problem instances taken from the literature shows the effectiveness of the proposed approach.     

An Efficient Genetic Algorithm for the p-Median Problem

We propose a new genetic algorithm for a well-known facility location problem. The algorithm is relatively simple and it generates good solutions quickly. Evolution is facilitated by a greedy heuristic. Computational tests with a total of 80 problems from four different sources with 100 to 1,000 nodes indicate that the best solution generated by the algorithm is within 0.1% of the optimum for 85% of the problems. The coding effort and the computational effort required are minimal, making the algorithm a good choice for practical applications requiring quick solutions, or for upper-bound generation to speed up optimal algorithms.     

An Efficient Algorithm for the Complex Roots Problem

Given a univariate polynomialf(z) of degreenwith complex coefficients, whose norms are less than 2^min magnitude, the root problem is to find all the roots off(z) up to specified precision 2^−μ. Assuming the arithmetic model for computation, we provide an algorithm which has complexityO(nlog⁵nlogB), whereb= χ + μ, and χ = max{n,m}. This improves on the previous best known algorithm of Pan for the problem, which has complexityO(n²log²nlog(m+ μ)). A remarkable property of our algorithm is that it does not require any assumptions about the root separation off, which were either explicitly, or implicitly, required by previous algorithms. Moreover it also has a work-efficient parallel implementation. We also show that both the sequential and parallel implementations of the algorithm work without modification in the Boolean model of arithmetic. In this case, it follows from root perturbation estimates that we need only specify θ = ⌈n(B+ logn+ 3)⌉ bits of the binary representations of the real and imaginary parts of each of the coefficients off. We also show that by appropriate rounding of intermediate values, we can bound the number of bits required to represent all complex numbers occurring as intermediate quantities in the computation. The result is that we can restrict the numbers we use in every basic arithmetic operation to those having real and imaginary parts with at most φ bits, where[formula]and[formula]Thus, in the Boolean model, the overall work complexity of the algorithm is only increased by a multiplicative factor ofM(φ) (whereM(ψ) =O(ψ(log ψ) log log ψ) is the bit complexity for multiplication of integers of length ψ). The key result on which the algorithm is based, is a new theorem of Coppersmith and Neff relating the geometric distribution of the zeros of a polynomial to the distribution of the zeros of its high order derivatives. We also introduce several new techniques (splitting sets and “centered” points) which hinge on it. We also observe that our root finding algorithm can be efficiently parallelized to run in parallel timeO(log⁶nlogB) usingnprocessors.     

Regularization Parameter Selection for the Low Rank Matrix Recovery

Journal of Optimization Theory and Applications - A popular approach to recover low rank matrices is the nuclear norm regularized minimization (NRM) for which the selection of the regularization...     

利用矩阵行秩生成概念格的一种算法

概念格是根据二元关系提出的一种概念层次结构,它描述了对象和属性的关系,利用矩阵行秩的层次思想提出了一种基于矩阵行秩的概念格生成算法,并用实例描述了对象和属性之间的概念关系.     

一类广义指派问题的有效解法

提出一类广义指派问题,这类问题研究的是m个人执行n项任务,每个人执行的任务数、执行每项任务的人数以及总的指派人项数均有限制,要求最优指派.对这类广义指派问题建立了数学模型,并找到一种转换方法,将这类问题转换为平衡指派问题,从而用传统方法,如匈牙利法求解.最后用一个箅例来说明这种转换方法的简便和有效性.     

An Efficient Algorithm for Solving the Rectilinear Multifacility Location Problem

One of the efficient methods for solving large rectilinear multifacilitylocation problems is the Direct Search method. The only drawbackof this method lies in the following difficulty. In some situations,when t new facilities are located together at one point, thenumber of arithmetic operations which are needed to establishoptimality is proportional to t²2^t. Therefore the method needsa prohibitive amount of computation time whenever t exceeds,say, 20. This paper gives a simple remedy for this problem.The paper states and proves a new necessary and sufficient optimalitycondition. This condition transforms the problem of computinga descent direction into a constrained linear least-squaresproblem. The latter problem is solved by a relaxation methodthat takes advantage of its special structure. The new techniqueis incorporated into the Direct Search method. This yields animproved algorithm that handles efficiently very large clusters.Numerical results are included.     

求解低秩矩阵填充的改进的交替最速下降法

矩阵填充是指利用矩阵的低秩特性而由部分观测元素恢复出原矩阵,在推荐系统、信号处理、医学成像、机器学习等领域有着广泛的应用。采用精确线搜索的交替最速下降法由于每次迭代计算量小因而对大规模问题的求解非常有效。本文在其基础上采用分离地精确线搜索,可使得每次迭代下降更多但计算量相同,从而可望进一步提高计算效率。本文分析了新算法的收敛性。数值结果也表明所提出的算法更加有效。     

Structured Low Rank Approximation of a Bezout Matrix

The task of determining the approximate greatest common divisor (GCD) of more than two univariate polynomials with inexact coefficients can be formulated as computing for a given Bezout matrix a new Bezout matrix of lower rank whose entries are near the corresponding entries of that input matrix. We present an algorithm based on a version of structured nonlinear total least squares (SNTLS) method for computing approximate GCD and demonstrate the practical performance of our algorithm on a diverse set of univariate polynomials. The work is partially supported by a National Key Basic Research Project of China 2004CB318000 and Chinese National Science Foundation under Grant 10401035.     

Dykstra's Algorithm for a Constrained Least-squares Matrix Problem

We apply Dykstra's alternating projection algorithm to the constrained least-squares matrix problem that arises naturally in statistics and mathematical economics. In particular, we are concerned with the problem of finding the closest symmetric positive definite bounded and patterned matrix, in the Frobenius norm, to a given matrix. In this work, we state the problem as the minimization of a convex function over the intersection of a finite collection of closed and convex sets in the vector space of square matrices. We present iterative schemes that exploit the geometry of the problem, and for which we establish convergence to the unique solution. Finally, we present preliminary numberical results to illustrate the performance of the proposed iterative methods.     

带非凸二次约束的二次比式和问题的全局优化算法(英文)

对带非凸二次约束的二次比式和问题(P)给出分枝定界算法,首先将问题(P)转化为其等价问题(Q),然后利用线性化技术,建立了(Q)松弛线性规划问题(RLP),通过对(RLP)可行域的细分及求解一系列线性规划问题,不断更新(Q)的上下界,从理论上证明了算法的收敛性,数值实验表明了算法的可行性和有效性.     

求解运输问题的一种算法

文章给出了运输问题的一种算法,该算法计算过程容易掌握,求解具有一次终止性     

Fuzzy关系矩阵传递闭包的截矩阵-Warshall算法

根据模糊矩阵的截矩阵性质,提出了利用截矩阵求模糊关系矩阵传递闭包的一种新算法。     

An Algorithm for Limited Capacity Inventory Problem with Staggering

This paper describes an algorithm for solving the integer programming problem, which arises in the staggering approach to limited capacity inventory systems. Numerical examples are given.     

斜对称双对角矩阵特征值反问题

讨论了关于斜对称双对角矩阵的特征值反问题．即：已知一个n阶斜对称双对角矩阵的特征值和两个n-1阶子矩阵的部分特征值，则可求得该矩阵．最后给出了数值例子．     

An Algorithm for the Allocation Problem

This paper* sets out a procedure for solving allocation problems, on different lines from procedures based on linear programming.     

An Algorithm for the Vehicle-dispatching Problem

The vehicle-scheduling problem involves the design of several vehicle tours to meet a given set of requirements for customers with known locations, subject to a capacity constraint for the vehicles and a distance (or time) constraint for vehicle tours. Three methods of solution are considered in this paper:

1. a
   A branch-and-bound approach.
    
2. b
   The "savings" approach.
    
3. c
   The 3-optimal tour method.
    

The excessive computation time and computer storage required for the first method renders it impracticable for large problems. Ten problems are examined and the results suggest that method C is superior to the other two methods.     

An Algorithm for Reducing the Matrix Norm

Based on the singular decomposition of $2×2$ matrix an algorithm for reducing the matrix norm is presented. Under the optimal choice of the parameters the matrix B after transformation may be considered as "Locally normal", that is, the four corresponding elements of $BB^T-B^TB$ are zero.     

Low Rank Approximation of a Hankel Matrix by Structured Total Least Norm

The structure preserving rank reduction problem arises in many important applications. The singular value decomposition (SVD), while giving the closest low rank approximation to a given matrix in matrix L ₂ norm and Frobenius norm, may not be appropriate for these applications since it does not preserve the given structure. We present a new method for structure preserving low rank approximation of a matrix, which is based on Structured Total Least Norm (STLN). The STLN is an efficient method for obtaining an approximate solution to an overdetermined linear system AX B, preserving the given linear structure in the perturbation [E F] such that (A + E)X = B + F. The approximate solution can be obtained to minimize the perturbation [E F] in the L _p norm, where p = 1, 2, or . An algorithm is described for Hankel structure preserving low rank approximation using STLN with L _p norm. Computational results are presented, which show performances of the STLN based method for L ₁ and L ₂ norms for reduced rank approximation for Hankel matrices.     

An Efficient Enthalpy-type Method for the Stefan Problem

The paper presents a new finite-difference method for solvingthe one-dimensional two-phase Stefan problem. Under assumptionson the data which guarantee the temperature u and the movingboundary s to belong to and , respectively, we obtain L₂-errorestimates of order O(h + h^–?) provided the time step is chosen such that Numerical aspects are discussed.