Sequential quadratic optimization algorithms are proposed for solving smooth nonlinear problems with equality constraints. The main focus is an algorithm the case when the...

In this work a strong framework is presented for solving the constrained nonlinear optimization problem that is a relatively complicated problem. These problems arise in a diverse range of sciences. There are a number of different approaches have been proposed. In this work, we employ the imperialist competitive algorithm (ICA) for solving constrained nonlinear optimization problems. Some well-...

we present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step q-superlinear convergence. the approach is based on an adaptive structured scheme due to mahdavi-amiri and bartels of the exact penalty method of coleman and conn for nonlinearly constrained optimization problems. the structured adaptation also makes use of the ideas of n...

This paper considers the fixed point problem for a nonexpansive mapping on a real Hilbert space and proposes novel line search fixed point algorithms to accelerate the search. The termination conditions for the line search are based on the well-known Wolfe conditions that are used to ensure the convergence and stability of unconstrained optimization algorithms. The directions to search for fixe...

We review the main techniques used in trust region algorithms for nonlinear constrained optimization. 1. Trust Region Idea Constrained optimization is to minimize a function subject to finitely many algebraic equation and inequality conditions. It has the following form

In this paper, a Hopfiled neural network for nonlinear constrained optimization problem is discussed. The energy function for the nonlinear neural network with its neural dynamics is defined based on penalty function with two-order continuous differential. The system of the neural network is stable, and its equilibrium point of the neural dynamics is also an approximately solution for nonlinear...