In this paper, a globally convergent Newton-type proximal gradient method is developed for composite multi-objective optimization problems where each objective function can be represented as the sum of smooth and nonsmooth function. The proposed deals with unconstrained convex problems. This free from any kind priori chosen parameters or ordering information functions. At every iteration method...

This paper presents a tensor approximation algorithm, based on the Levenberg–Marquardt method for nonlinear least square problem, to decompose large-scale tensors into sum of products vector groups given scale, or obtain low-rank without losing too much accuracy. An Armijo-like rule inexact line search is also introduced this algorithm. The result decomposition adjustable, which implies that ca...

This paper extends the full convergence of the steepest descent algorithm with a generalized Armijo search and a proximal regularization to solve quasiconvex minimization problems defined on complete Riemannian manifolds. Previous convergence results are obtained as particular cases of our approach and some examples in non Euclidian spaces are given.

We consider the problem of accurate computation of the finite difference f(x+ s)− f(x) when ‖s‖ is very small. Direct evaluation of this difference in floating point arithmetic succumbs to cancellation error and yields 0 when s is sufficiently small. Nonetheless, accurate computation of this finite difference is required by many optimization algorithms for a “sufficient decrease” test. Reps and...

In this paper, an efficient recursive construction algorithm based on the backtracking search algorithm is presented for the construction of accurate implication sets. The new techniques suggested improve the time and space complexities of the backtracking search algorithm dramatically.

Feature selection carries significance in the outcome of any classification or regression task. Exercising evolutionary computation algorithms feature has led to construction efficient discrete optimization algorithms. In this paper, a modified backtracking search algorithm is employed perform wrapper-based selection, where two modifications standard are adopted. The first one concentrates on u...

Multi-Point Constructive Search maintains a small set of “elite solutions” that are used to heuristically guide constructive search through periodically restarting search from an elite solution. Empirical results indicate that for job shop scheduling optimization problems and quasi-group completion problems, multi-point constructive search performs significantly better than chronological backtr...