In this paper we present a new multilevel information sharing strategy within a swarm to handle single objective, constrained and unconstrained optimization problems. A swarm is a collection of individuals having a common goal to reach the best value (minimum or maximum) of a function. Among the individuals in a swarm, there are some better performers (leaders) those that set the direction of s...

In this paper, we study the structure from motion problem as a constrained nonlinear least squares problem which minimizes the so called reprojection error subject to all constraints among multiple images. By converting this constrained optimization problem to an unconstrained one, we obtain a multiview version of the normalized epipolar constraint of two views. Such a multiview normalized epip...

The conjugate gradient (CG) method is one of the most popular methods for solving smooth unconstrained optimization problems due to its simplicity and low memory requirement. However, the usage of CG methods are mainly restricted in solving smooth optimization problems so far. The purpose of this paper is to present efficient conjugate gradient-type methods to solve nonsmooth optimization probl...

The satissability problem (SAT) is a fundamental problem in mathematical logic, constraint satisfaction , VLSI engineering, and computing theory. Methods to solve the satissability problem play an important role in the development of computing theory and systems. Traditional methods treat the SAT problem as a constrained decision problem. During past research, the number of unsatissable clauses...

A new application for support vector machines is their use for meta-modeling feasible regions in constrained optimization problems. We here describe a solution for the still unsolved problem of a standardized integration of such models into (evolutionary) optimization algorithms with the help of a new decoder based approach. This goal is achieved by constructing a mapping function that maps the...

Training of recurrent neural networks is typically formulated as unconstrained optimization problems. There is, however, an implicit constraint stating that the equations of state must be satisfied at every iteration in the optimization process. Such constraints can make a problem highly non-linear and thus difficult to solve. A potential remedy is to reformulate the problem into one in which t...

Abstract. This paper studies a regularization approach for simultaneously reconstructing spacetime dependent Robin coefficient γ(x, t) and heat flux q(x, t). The differentiability results and adjoint systems are established. A standard finite element method (FEM) is employed to discretize the constrained optimization problem which is reduced to a sequence of unconstrained optimization problem b...

This paper proposes a closed form solution to L2-sensitivity minimization of 2nd-order statespace digital filters subject to L2-scaling constraints. The proposed solution reduces a constrained optimization problem to an unconstrained optimization problem by appropriate variable transformation. Furthermore, by restricting ourselves to the case of 2nd-order statespace digital filters, we can form...