A new iterative Monte Carlo approach for inverse matrix problem

A new approach of iterative Monte Carlo algorithms for the well-known inverse matrix problem is presented and studied. The algorithms are based on a special techniques of iteration parameter choice, which allows to control the convergence of the algorithm for any column (row) of the matrix using different relaxation parameters. The choice of these parameters is controlled by a posteriori criteria for every Monte Carlo iteration. The presented Monte Carlo algorithms are implemented on a SUN Sparkstation. Numerical tests are performed for matrices of moderate in order to show how work the algorithms. The algorithms under consideration are well parallelized.     

Evaluation of the multiobjective ant colony algorithm performances on biobjective quadratic assignment problems

The difficulty of resolving the multiobjective combinatorial optimization problems with traditional methods has directed researchers to investigate new approaches which perform better. In recent years some algorithms based on ant colony optimization (ACO) metaheuristic have been suggested to solve these multiobjective problems. In this study these algorithms have been reported and programmed both to solve the biobjective quadratic assignment problem (BiQAP) instances and to evaluate the performances of these algorithms. The robust parameter sets for each 12 multiobjective ant colony optimization (MOACO) algorithms have been calculated and BiQAP instances in the literature have been solved within these parameter sets. The performances of the algorithms have been evaluated by comparing the Pareto fronts obtained from these algorithms. In the evaluation step, a multi significance test is used in a non hierarchical structure, and a performance metric (P metric) essential for this test is introduced. Through this study, decision makers will be able to put in the biobjective algorithms in an order according to the priority values calculated from the algorithms’ Pareto fronts. Moreover, this is the first time that MOACO algorithms have been compared by solving BiQAPs.     

A comparison of sequential Delaunay triangulation algorithms

This paper presents an experimental comparison of a number of different algorithms for computing the Delaunay triangulation. The algorithms examined are: Dwyer's divide and conquer algorithm, Fortune's sweepline algorithm, several versions of the incremental algorithm (including one by Ohya, Iri and Murota, a new bucketing-based algorithm described in this paper, and Devillers's version of a Delaunay-tree based algorithm that appears in LEDA), an algorithm that incrementally adds a correct Delaunay triangle adjacent to a current triangle in a manner similar to gift wrapping algorithms for convex hulls, and Barber's convex hull based algorithm.

Most of the algorithms examined are designed for good performance on uniformly distributed sites. However, we also test implementations of these algorithms on a number of non-uniform distributions. The experiments go beyond measuring total running time, which tends to be machine-dependent. We also analyze the major high-level primitives that algorithms use and do an experimental analysis of how often implementations of these algorithms perform each operation.     

Self-adaptive differential evolution incorporating a heuristic mixing of operators

A considerable number of differential evolution variants have been proposed in the last few decades. However, no variant was able to consistently perform over a wide range of test problems. In this paper, propose two novel differential evolution based algorithms are proposed for solving constrained optimization problems. Both algorithms utilize the strengths of multiple mutation and crossover operators. The appropriate mix of the mutation and crossover operators, for any given problem, is determined through an adaptive learning process. In addition, to further accelerate the convergence of the algorithm, a local search technique is applied to a few selected individuals in each generation. The resulting algorithms are named as Self-Adaptive Differential Evolution Incorporating a Heuristic Mixing of Operators. The algorithms have been tested by solving 60 constrained optimization test instances. The results showed that the proposed algorithms have a competitive, if not better, performance in comparison to the-state-of-the-art algorithms.     

Maxima-finding algorithms for multidimensional samples: A two-phase approach

Simple, two-phase algorithms are devised for finding the maxima of multidimensional point samples, one of the very first problems studied in computational geometry. The algorithms are easily coded and modified for practical needs. The expected complexity of some measures related to the performance of the algorithms is analyzed. We also compare the efficiency of the algorithms with a few major ones used in practice, and apply our algorithms to find the maximal layers and the longest common subsequences of multiple sequences.     

Hybridizing local search algorithms for global optimization

In this paper, we combine two types of local search algorithms for global optimization of continuous functions. In the literature, most of the hybrid algorithms are produced by combination of a global optimization algorithm with a local search algorithm and the local search is used to improve the solution quality, not to explore the search space to find independently the global optimum. The focus of this research is on some simple and efficient hybrid algorithms by combining the Nelder–Mead simplex (NM) variants and the bidirectional random optimization (BRO) methods for optimization of continuous functions. The NM explores the whole search space to find some promising areas and then the BRO local search is entered to exploit optimal solution as accurately as possible. Also a new strategy for shrinkage stage borrowed from differential evolution (DE) is incorporated in the NM variants. To examine the efficiency of proposed algorithms, those are evaluated by 25 benchmark functions designed for the special session on real-parameter optimization of CEC2005. A comparison study between the hybrid algorithms and some DE algorithms and non-parametric analysis of obtained results demonstrate that the proposed algorithms outperform most of other algorithms and their difference in most cases is statistically considerable. In a later part of the comparative experiments, a comparison of the proposed algorithms with some other evolutionary algorithms reported in the CEC2005 confirms a better performance of our proposed algorithms.     

Local elimination algorithms for solving sparse discrete problems

The class of local elimination algorithms is considered that make it possible to obtain global information about solutions of a problem using local information. The general structure of local elimination algorithms is described that use neighborhoods of elements and the structural graph describing the problem structure; an elimination algorithm is also described. This class of algorithms includes local decomposition algorithms for discrete optimization problems, nonserial dynamic programming algorithms, bucket elimination algorithms, and tree decomposition algorithms. It is shown that local elimination algorithms can be used for solving optimization problems.     

Algorithms for the Laplace–Stieltjes Transforms of First Return Times for Stochastic Fluid Flows

We derive several algorithms, including quadratically convergent algorithms, which can be used to calculate the Laplace–Stieltjes transforms of the time taken to return to the initial level in the Markovian stochastic fluid flow model. We give physical interpretations of the algorithms and consider their numerical analysis. The numerical performance of the algorithms, which depends on the physical properties of the process, is discussed and illustrated with simple examples. Besides the powerful algorithms, this paper contributes interesting theoretical results. In particular, the methodology for constructing these algorithms is a valuable contribution to the theory of fluid flow models. Moreover, useful physical interpretations of the algorithms, and related expressions, given in terms of the fluid flow model, can assist in further analysis and help in a better understanding of the model. The authors would like to thank the Australian Research Council for funding this research through Discovery Project DP0770388.     

Decomposition Methods for Differentiable Optimization Problems over Cartesian Product Sets

This paper presents a unified analysis of decomposition algorithms for continuously differentiable optimization problems defined on Cartesian products of convex feasible sets. The decomposition algorithms are analyzed using the framework of cost approx imation algorithms. A convergence analysis is made for three decomposition algorithms: a sequential algorithm which extends the classical Gauss-Seidel scheme, a synchronized parallel algorithm which extends the Jacobi method, and a partially asynchronous parallel algorithm. The analysis validates inexact computations in both the subproblem and line search phases, and includes convergence rate results. The range of feasible step lengths within each algorithm is shown to have a direct correspondence to the increasing degree of parallelism and asynchronism, and the resulting usage of more outdated information in the algorithms.     

Homogeneous algorithms for multiextremal optimization

The class of homogeneous algorithms for multiextremal optimization is defined, and a number of theorems are proved, including a sufficient condition for the convergence of homogeneous algorithms to a global minimizer. An approach to the synthesis of homogeneous algorithms based on model multi-peak functions is proposed. The existing algorithms are reviewed, and a new efficient multidimensional algorithm based on the Delaunay triangulation is constructed. Some numerical results are presented.     

Rethinking the role of algorithms in school mathematics: a conceptual model with focus on cognitive development

Starting from the context of mathematics learning in the East and West, this paper discusses the position and role of algorithms within school mathematics and argues that learning of algorithms has suffered from an alleged dichotomy between procedures and understanding, in that algorithms have been associated with low-level cognition. The paper first introduces a broad perspective about algorithms in school mathematics, and then, partially drawing on Bloom’s taxonomy and Säljö’s categorization of learning, proposes a model for the learning of algorithms with focus on students’ cognitive development. The model consists of three cognitive levels: (1) Knowledge and Skills, (2) Understanding and Comprehension, and (3) Evaluation and Construction. The model suggests that the learning of algorithms does not simply imply a low level of cognition, and provides a new perspective and framework to analyse the learning of algorithms. Following the model, we present examples to demonstrate the three levels and discuss related teaching strategies. We propose that the model can be used as an analysis tool to reconceptualize the role of algorithms in school mathematics and pose some questions for further research and scholarly discourse in this direction.     

An evolutionary algorithm with the partial sequential forward floating search mutation for large-scale feature selection problems

Several meta-heuristic algorithms, such as evolutionary algorithms (EAs) and genetic algorithms (GAs), have been developed for solving feature selection problems due to their efficiency for searching feature subset spaces in feature selection problems. Recently, hybrid GAs have been proposed to improve the performance of conventional GAs by embedding a local search operation, or sequential forward floating search mutation, into the GA. Existing hybrid algorithms may damage individuals’ genetic information obtained from genetic operations during the local improvement procedure because of a sequential process of the mutation operation and the local improvement operation. Another issue with a local search operation used in the existing hybrid algorithms is its inappropriateness for large-scale problems. Therefore, we propose a novel approach for solving large-sized feature selection problems, namely, an EA with a partial sequential forward floating search mutation (EAwPS). The proposed approach integrates a local search technique, that is, the partial sequential forward floating search mutation into an EA method. Two algorithms, EAwPS-binary representation (EAwPS-BR) for medium-sized problems and EAwPS-integer representation (EAwPS-IR) for large-sized problems, have been developed. The adaptation of a local improvement method into the EA speeds up the search and directs the search into promising solution areas. We compare the performance of the proposed algorithms with other popular meta-heuristic algorithms using the medium- and large-sized data sets. Experimental results demonstrate that the proposed EAwPS extracts better features within reasonable computational times.     

New algorithms for selection

New algorithms are presented to select the k largest elements, and give their respective order, of a totally ordered set of n elements, when k is small compared to n. The performance of these algorithms improves over that of previously known algorithms. One of these algorithms is optimal for a wide range of values of n and k. The algorithms can be modified to select the k th largest element only. The performance of the modified algorithms improves, for asymptotic values of n, over that of previously known algorithms for selecting the k th largest element.     

Monte Carlo Algorithms for Identifying Densely Connected Subgraphs

The problem of finding densely connected subgraphs in a network has attracted a lot of recent interest. Such subgraphs are sometimes referred to as communities in social networks or molecular modules in protein networks. In this article, we propose two Monte Carlo optimization algorithms for identifying the densest subgraphs with a fixed size or with size in a given range. The new algorithms combine the idea of simulated annealing and efficient moves for the Markov chain, and both algorithms are shown to converge to the set of optimal states (densest subgraphs) with probability 1. When applied to a yeast protein interaction network and a stock market graph, the algorithms identify interesting new densely connected subgraphs. Supplementary materials for the article are available online.     

New subgradient extragradient methods for common solutions to equilibrium problems

In this paper, three parallel hybrid subgradient extragradient algorithms are proposed for finding a common solution of a finite family of equilibrium problems in Hilbert spaces. The proposed algorithms originate from previously known results for variational inequalities and can be considered as modifications of extragradient methods for equilibrium problems. Theorems of strong convergence are established under the standard assumptions imposed on bifunctions. Some numerical experiments are given to illustrate the convergence of the new algorithms and to compare their behavior with other algorithms.     

Mesh adaptive direct search algorithms for mixed variable optimization

This paper introduces a new derivative-free class of mesh adaptive direct search (MADS) algorithms for solving constrained mixed variable optimization problems, in which the variables may be continuous or categorical. This new class of algorithms, called mixed variable MADS (MV-MADS), generalizes both mixed variable pattern search (MVPS) algorithms for linearly constrained mixed variable problems and MADS algorithms for general constrained problems with only continuous variables. The convergence analysis, which makes use of the Clarke nonsmooth calculus, similarly generalizes the existing theory for both MVPS and MADS algorithms, and reasonable conditions are established for ensuring convergence of a subsequence of iterates to a suitably defined stationary point in the nonsmooth and mixed variable sense.     

An implementable proximal point algorithmic framework for nuclear norm minimization

The nuclear norm minimization problem is to find a matrix with the minimum nuclear norm subject to linear and second order cone constraints. Such a problem often arises from the convex relaxation of a rank minimization problem with noisy data, and arises in many fields of engineering and science. In this paper, we study inexact proximal point algorithms in the primal, dual and primal-dual forms for solving the nuclear norm minimization with linear equality and second order cone constraints. We design efficient implementations of these algorithms and present comprehensive convergence results. In particular, we investigate the performance of our proposed algorithms in which the inner sub-problems are approximately solved by the gradient projection method or the accelerated proximal gradient method. Our numerical results for solving randomly generated matrix completion problems and real matrix completion problems show that our algorithms perform favorably in comparison to several recently proposed state-of-the-art algorithms. Interestingly, our proposed algorithms are connected with other algorithms that have been studied in the literature.     

Multiple UAVs path planning algorithms: a comparative study

Unmanned aerial vehicles (UAVs) are used in team for detecting targets and keeping them in its sensor range. There are various algorithms available for searching and monitoring targets. The complexity of the search algorithm increases if the number of nodes is increased. This paper focuses on multi UAVs path planning and Path Finding algorithms. Number of Path Finding and Search algorithms was applied to various environments, and their performance compared. The number of searches and also the computation time increases as the number of nodes increases. The various algorithms studied are Dijkstra’s algorithm, Bellman Ford’s algorithm, Floyd-Warshall’s algorithm and the AStar algorithm. These search algorithms were compared. The results show that the AStar algorithm performed better than the other search algorithms. These path finding algorithms were compared so that a path for communication can be established and monitored.     

Fast algorithms for floating-point interval matrix multiplication

We discuss several methods for real interval matrix multiplication. First, earlier studies of fast algorithms for interval matrix multiplication are introduced: naive interval arithmetic, interval arithmetic by midpoint-radius form by Oishi-Rump and its fast variant by Ogita-Oishi. Next, three new and fast algorithms are developed. The proposed algorithms require one, two or three matrix products, respectively. The point is that our algorithms quickly predict which terms become dominant radii in interval computations. We propose a hybrid method to predict which algorithm is suitable for optimizing performance and width of the result. Numerical examples are presented to show the efficiency of the proposed algorithms.     

Zur bestimmung aller elementarkreise in gerichteten graphen

New algorithms for enumerating all circuits of a directed graph are presented. These algorithms are backtrack algorithms, by which we intended to avoid fruitless computations. The best results give algorithms involving heuristic rules.

To receive numerical comparisons additionally the algorithms of DÖRFLER/MÜHL-BACHER, TIERNAN/SYSLO and BJELKINA were programmed and tested in FORTRAN (CDC 3300) and (or) ALGOL (ICL 4130).