‎the global fom and gmres algorithms are among the effective‎ ‎methods to solve sylvester matrix equations‎. ‎in this paper‎, ‎we‎ ‎study these algorithms in the case that the coefficient matrices‎ ‎are real symmetric (real symmetric positive definite) and extract‎ ‎two cg-type algorithms for solving generalized sylvester matrix‎ ‎equations‎. ‎the proposed methods are iterative projection metho...

this study proposes a modified version of cultural algorithms (cas) which benefits from rule-based system for influence function. this rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. this is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. this rule ...

the cuckoo search algorithm is a recently developedmeta-heuristic optimization algorithm, which is suitable forsolving optimization problems. to enhance the accuracy andconvergence rate of this algorithm, an improved cuckoo searchalgorithm is proposed in this paper. normally, the parametersof the cuckoo search are kept constant. this may lead todecreasing the efficiency of the algorithm. to cop...

in this paper, a new continuous ant colony optimization (caco) algorithm is proposed for optimal reservoir operation. the paper presents a new method of determining and setting a complete set of control parameters for any given problem, saving the user from a tedious trial and error based approach to determine them. the paper also proposes an elitist strategy for caco algorithm where best solut...

In this paper, we seek the conjugate gradient direction closest to the direction of the scaled memoryless BFGS method and propose a family of conjugate gradient methods for unconstrained optimization. An improved Wolfe line search is also proposed, which can avoid a numerical drawback of the Wolfe line search and guarantee the global convergence of the conjugate gradient method under mild condi...

Global optimization is necessary in some cases when we want to achieve the best solution or we require a new solution which is better the old one. However global optimization is a hazard problem. Gradient descent method is a well-known technique to find out local optimizer whereas approximation solution approach aims to simplify how to solve the global optimization problem. In order to find out...

A new meta-heuristic method, based on Neuronal Communication (NC), is introduced in this article. The neuronal communication illustrates how data is exchanged between neurons in neural system. Actually, this pattern works efficiently in the nature. The present paper shows it is the same to find the global minimum. In addition, since few numbers of neurons participate in each step of the method,...

in this paper, an iterative method is proposed for solving large general sylvester matrix equation $axb+cxd = e$, where $a in r^{ntimes n}$ , $c in r^{ntimes n}$ , $b in r^{stimes s}$ and  $d in r^{stimes s}$ are given matrices and $x in r^{stimes s}$  is the unknown matrix. we present a global conjugate gradient (gl-cg) algo- rithm for solving linear system of equations with multiple right-han...

We present a gradient-tree-boosting-based structured learning model for jointly disambiguating named entities in a document. Gradient tree boosting is a widely used machine learning algorithm that underlies many topperforming natural language processing systems. Surprisingly, most works limit the use of gradient tree boosting as a tool for regular classification or regression problems, despite ...

The quantum basin hopping algorithm for continuous global optimisation combines a local search with Grover’s algorithm, and can locate the global optimum using effort proportional to the square root of the number of basins. This article establishes that Jordan’s quantum gradient estimation method can be incorporated into the quantum basin hopper, providing an extra acceleration proportional to ...