This paper presents a parallel version of a Generalized Conjugate Gradient algorithm proposed by Liu and Story in which the search direction considers the effect of the inexact line search. We describe the implementation of this algorithm on a parallel architecture and analyze the related speedup ratios. Numerical results are given for a shared memory computer (Cray C92).

We propose two algorithms for nonconvex unconstrained optimization problems that employ Polak-Ribière-Polyak conjugate gradient formula and new inexact line search techniques. We show that the new algorithms converge globally if the function to be minimized has Lipschitz continuous gradients. Preliminary numerical results show that the proposed methods for particularly chosen line search condit...

Pattern matching occurs in various applications, ranging from simple text searching in word processors to identification of common motifs in DNA sequences in computational biology. The problem of exact pattern matching has been well studied and a number of efficient algorithms exist. However these exact pattern matching algorithms are of little help when they are applied to finding patterns in ...

In this paper, we present a QP-free algorithm for nonlinear semidefinite programming. At each iteration, the search direction is yielded by solving two systems of linear equations with the same coefficient matrix; [Formula: see text] penalty function is used as merit function for line search, the step size is determined by Armijo type inexact line search. The global convergence of the proposed ...

This paper proposes a history-based structured learning approach that jointly extracts entities and relations in a sentence. We introduce a novel simple and flexible table representation of entities and relations. We investigate several feature settings, search orders, and learning methods with inexact search on the table. The experimental results demonstrate that a joint learning approach sign...

In this paper, a new hybrid conjugate gradient algorithm is proposed for solving unconstrained optimization problems. This new method can generate sufficient descent directions unrelated to any line search. Moreover, the global convergence of the proposed method is proved under the Wolfe line search. Numerical experiments are also presented to show the efficiency of the proposed algorithm, espe...

In this paper, we prove the global convergence of the Perry-Shanno’s memoryless quasi-Newton (PSMQN) method with a new inexact line search when applied to nonconvex unconstrained minimization problems. Preliminary numerical results show that the PSMQN with the particularly line search conditions are very promising.

Exact and inexact methods can be used for solving Constraint Satisfaction Problems (CSP), i.e. for finding a variable assignment which violates none of the constraints or minimizes the number of violated constraints. Based on a Backtrack tree search, exact methods are able to produce an optimal assignment, when no time limit is imposed. Based on local improvement mechanisms, inexact methods can...