Simulation of a nonlinear Steklov eigenvalue problem using finite-element approximation

Elliptic problems with parameters in the boundary conditions are called Steklov problems. With the tool of computational approximation (finite-element method), we estimate the solution of a nonlinear Steklov eigenvalue problem for a second-order, self-adjoint, elliptic differential problem. We discussed the behavior of the nonlinear problem with the help of computational results using Matlab.     

2DCPackGen: A problem generator for two-dimensional rectangular cutting and packing problems

Cutting and packing problems have been extensively studied in the literature in recent decades, mainly due to their numerous real-world applications while at the same time exhibiting intrinsic computational complexity. However, a major limitation has been the lack of problem generators that can be widely and commonly used by all researchers in their computational experiments. In this paper, a problem generator for every type of two-dimensional rectangular cutting and packing problems is proposed. The problems are defined according to the recent typology for cutting and packing problems proposed by Wäscher, Haußner, and Schumann (2007) and the relevant problem parameters are identified. The proposed problem generator can significantly contribute to the quality of the computational experiments run with cutting and packing problems and therefore will help improve the quality of the papers published in this field.     

The reformulation of two mixed integer programming problems

Two practical problems are described, each of which can be formulated in more than one way as a mixed integer programming problem. The computational experience with two formulations of each problem is given. It is pointed out how in each case a reformulation results in the associated linear programming problem being more constrained. As a result the reformulated mixed integer problem is easier to solve. The problems are a multi-period blending problem and a mining investment problem.     

Heuristics and reduction methods for multiple constraints 0–1 linear programming problems

This work extends the efficient results relative to the 0–1 knapsack problem to the multiple inequality constraints 0–1 linear programming problems. The two crucial phases for the solving of this type of problems are presented: (i) Two linear expected time complexity greedy algorithms are proposed for the determination of a lower bound on the optimal value by using a cascade of surrogate relaxations of the original problem whose sizes are decreasing step by step. A comparative study with the best known heuristic methods is reported; it concerned the accuracy of the approximate solutions and the practical computational times. (ii) This greedy algorithm is inserted in an efficient reduction framework. Variables and constraints are eliminated by the conjunction of tests applied to Lagrangean and surrogate relaxations of the original problem. A lot of computational results are summarized by considering test problems of the literature.     

A collocation/quadrature-based Sturm–Liouville problem solver

We present a computational method for solving a class of boundary-value problems in Sturm–Liouville form. The algorithms are based on global polynomial collocation methods and produce discrete representations of the eigenfunctions. Error control is performed by evaluating the eigenvalue problem residuals generated when the eigenfunctions are interpolated to a finer discretization grid; eigenfunctions that produce residuals exceeding an infinity-norm bound are discarded. Because the computational approach involves the generation of quadrature weights and arrays for discrete differentiation operations, our computational methods provide a convenient framework for solving boundary-value problems by eigenfunction expansion and other projection methods.     

Scatter Search—Wellsprings and Challenges

This article is concerned with two global optimization problems (P1) and (P2). Each of these problems is a fractional programming problem involving the maximization of a ratio of a convex function to a convex function, where at least one of the convex functions is a quadratic form. First, the article presents and validates a number of theoretical properties of these problems. Included among these properties is the result that, under a mild assumption, any globally optimal solution for problem (P1) must belong to the boundary of its feasible region. Also among these properties is a result that shows that problem (P2) can be reformulated as a convex maximization problem. Second, the article presents for the first time an algorithm for globally solving problem (P2). The algorithm is a branch and bound algorithm in which the main computational effort involves solving a sequence of convex programming problems. Convergence properties of the algorithm are presented, and computational issues that arise in implementing the algorithm are discussed. Preliminary indications are that the algorithm can be expected to provide a practical approach for solving problem (P2), provided that the number of variables is not too large.     

The Extragradient Method for Convex Optimization in the Presence of Computational Errors

In this article, we study convergence of the extragradient method for constrained convex minimization problems in a Hilbert space. Our goal is to obtain an ε-approximate solution of the problem in the presence of computational errors, where ε is a given positive number. Most results known in the literature establish convergence of optimization algorithms, when computational errors are summable. In this article, the convergence of the extragradient method for solving convex minimization problems is established for nonsummable computational errors. We show that the the extragradient method generates a good approximate solution, if the sequence of computational errors is bounded from above by a constant.     

On computational search for optimistic solutions in bilevel problems

The linear-linear and quadratic-linear bilevel programming problems are considered. Their optimistic statement is reduced to a nonconvex mathematical programming problem with the bilinear structure. Approximate algorithms of local and global search in the obtained problems are proposed. The results of computational solving randomly generated test problems are given and analyzed.     

Comparative evaluation of MILP flowshop models

This paper investigates the performance of two families of mixed-integer linear programing (MILP) models for solving the regular permutation flowshop problem to minimize makespan. The three models of the Wagner family incorporate the assignment problem while the five members of the Manne family use pairs of dichotomous constraints, or their mathematical equivalents, to assign jobs to sequence positions. For both families, the problem size complexity and computational time required to optimally solve a common set of problems are investigated. In so doing, this paper extends the application of MILP approaches to larger problem sizes than those found in the existing literature. The Wagner models require more than twice the binary variables and more real variables than do the Manne models, while Manne models require more constraints for the same sized problems. All Wagner models require much less computational time than any of the Manne models for solving the common set of problems, and these differences increase dramatically with increasing number of jobs and machines. Wagner models can solve problems containing larger numbers of machines and jobs than the Manne models, and hence are preferable for finding optimal solutions to the permutation flowshop problem with makespan objective.     

An Unconstrained Quadratic Binary Programming Approach to the Vertex Coloring Problem

The vertex coloring problem has been the subject of extensive research for many years. Driven by application potential as well as computational challenge, a variety of methods have been proposed for this difficult class of problems. Recent successes in the use of the unconstrained quadratic programming (UQP) model as a unified framework for modeling and solving combinatorial optimization problems have motivated a new approach to the vertex coloring problem. In this paper we present a UQP approach to this problem and illustrate its attractiveness with preliminary computational experience.     

Two-level decomposition algorithm for crew rostering problems with fair working condition

A typical railway crew scheduling problem consists of two phases: a crew pairing problem to determine a set of crew duties and a crew rostering problem. The crew rostering problem aims to find a set of rosters that forms workforce assignment of crew duties and rest periods satisfying several working regulations. In this paper, we present a two-level decomposition approach to solve railway crew rostering problem with the objective of fair working condition. To reduce computational efforts, the original problem is decomposed into the upper-level master problem and the lower-level subproblem. The subproblem can be further decomposed into several subproblems for each roster. These problems are iteratively solved by incorporating cuts into the master problem. We show that the relaxed problem of the master problem can be formulated as a uniform parallel machine scheduling problem to minimize makespan, which is NP-hard. An efficient branch-and-bound algorithm is applied to solve the master problem. Effective valid cuts are developed to reduce feasible search space to tighten the duality gap. Using data provided by the railway company, we demonstrate the effectiveness of the proposed method compared with that of constraint programming techniques for large-scale problems through computational experiments.     

A genetic algorithm for solving linear fractional bilevel problems

Bilevel programming has been proposed for dealing with decision processes involving two decision makers with a hierarchical structure. They are characterized by the existence of two optimization problems in which the constraint region of the upper level problem is implicitly determined by the lower level optimization problem. In this paper a genetic algorithm is proposed for the class of bilevel problems in which both level objective functions are linear fractional and the common constraint region is a bounded polyhedron. The algorithm associates chromosomes with extreme points of the polyhedron and searches for a feasible solution close to the optimal solution by proposing efficient crossover and mutation procedures. The computational study shows a good performance of the algorithm, both in terms of solution quality and computational time.     

Cross decomposition applied to the stochastic transportation problem

In this paper we give a solution method for the stochastic transportation problem based on Cross Decomposition developed by Van Roy (1980). Solution methods to the derived sub and master problems are discussed and computational results are given for a number of large scale test problems. We also compare the efficiency of the method with other methods suggested for the stochastic transportation problem: The Frank-Wolfe algorithm and separable programming.     

LP based heuristics for the multiple knapsack problem with assignment restrictions

Starting with a problem in wireless telecommunication, we are led to study the multiple knapsack problem with assignment restrictions. This problem is NP-hard. We consider special cases and their computational complexity. We present both randomized and deterministic LP based algorithms, and show both theoretically and computationally their usefulness for large-scale problems.     

Epsilon-ritz method for solving optimal control problems: Useful parallel solution method

Using Balakrishnan's epsilon problem formulation (Ref. 1) and the Rayleigh-Ritz method with an orthogonal polynomial function basis, optimal control problems are transformed from the standard two-point boundary-value problem to a nonlinear programming problem. The resulting matrix-vector equations describing the optimal solution have standard parallel solution methods for implementation on parallel processor arrays. The method is modified to handle inequality constraints, and some results are presented under which specialized nonlinear functions, such as sines and cosines, can be handled directly. Some computational results performed on an Intel Sugarcube are presented to illustrate that considerable computational savings can be realized by using the proposed solution method.     

On the Use of Exact and Heuristic Cutting Plane Methods for the Quadratic Assignment Problem

This paper uses the formulation of the quadratic assignment problem as that of minimizing a concave quadratic function over the assignment polytope. Cutting plane procedures are investigated for solving this problem. A lower bound derived on the number of cuts needed for termination indicates that conventional cutting plane procedures would require a huge computational effort for the exact solution of the quadratic assignment problems. However, several heuristics which are derived from the cutting planes produce optimal or good quality solutions early on in the search process. An illustrative example and computational results are presented.     

Bin packing and multiprocessor scheduling problems with side constraint on job types

This paper deals with the bin packing problem and the multiprocessor scheduling problem both with an additional constraint specifying the maximum number of jobs in each type to the processed on a processor. Since these problems are NP-complete, various approximation algorithms are proposed by generalizing those algorithms known for the ordinary bin packing and multiprocessor scheduling problems. The worst-case performance of the proposed algorithms are analyzed, and some computational results are reported to indicate their average case behavior.     

A three-machine permutation flow-shop problem with minimum makespan on the second machine

In this paper, we consider a three-machine permutation flow-shop scheduling problem where the criterion is to minimize the total completion time without idle times subject to the minimum makespan on the second machine. This problem is NP-hard while each of the objective functions alone can be optimized in polynomial time. We develop a branch-and-bound algorithm with effective branching rules and dominance properties which help to reduce the search space. By our computational experiments, the branch-and-bound algorithm is comparable to a recent mixed integer programming solver and, for some kinds of problem instances, the branch-and-bound algorithm outperforms the solver. On the other hand, the computational result would indicate that the hierarchical scheduling problems are essentially hard in both theoretical and computational points of view.     

The square root Diffie–Hellman problem

Many cryptographic schemes are based on computationally hard problems. The computational Diffie–Hellman problem is the most well-known hard problem and there are many variants of it. Two of them are the square Diffie–Hellman problem and the square root Diffie–Hellman problem. There have been no known reductions from one problem to the other in either direction. In this paper we show that these two problems are polynomial time equivalent under a certain condition. However, this condition is weak, and almost all of the parameters of cryptographic schemes satisfy this condition. Therefore, our reductions are valid for almost all cryptographic schemes.     

NUMERICAL SOLUTIONS OF PARABOLIC PROBLEMS ON UNBOUNDED 3-D SPATIAL DOMAIN

In this paper,the numerical solutions of heat equation on 3-D unbounded spatial do-main are considered. n artificial boundary Γ is introduced to finite the computationaldomain.On the artificial boundary Γ,the exact boundary condition and a series of approx-imating boundary conditions are derived,which are called artificial boundary conditions.By the exact or approximating boundary condition on the artificial boundary,the originalproblem is reduced to an initial-boundary value problem on the bounded computationaldomain,which is equivalent or approximating to the original problem.The finite differencemethod and finite element method are used to solve the reduced problems on the finitecomputational domain.The numerical results demonstrate that the method given in thispaper is effective and feasible.