We propose an optimal control approach to tackle large scale unconstrained optimization problems. Our discussion begins with the introduction of a new framework to describe and deene the structure of a general function with a large number of variables. Based on the new concepts, a suucient condition is established so that any large scale unconstrained optimization problem satisfying this suucie...

In this paper, we formulate neural-network training as a constrained optimization problem instead of the traditional formulation based on unconstrained optimization. We show that constraints violated during a search provide additional force to help escape from local minima using our newly developed constrained simulated annealing (CSA) algorithm. We demonstrate the merits of our approach by tra...

Inference problems in graphical models are often approximated by casting them as constrained optimization problems. Message passing algorithms, such as belief propagation, have previously been suggested as methods for solving these optimization problems. However, there are few convergence guarantees for such algorithms, and the algorithms are therefore not guaranteed to solve the corresponding ...

Considering a general vector optimization problem, we attach to it two new vector duals by means of perturbation theory. These vector duals are constructed with the help of the recent Wolfe and Mond-Weir scalar duals for general optimization problems proposed by R.I. Boţ and S.-M. Grad, by exploiting an idea due to W. Breckner and I. Kolumbán. Constrained and unconstrained vector optimization p...

In recent papers, the problem of estimating the thickness and the optical constants (refractive index and absorption coefficient) of thin films using only transmittance data has been addressed by means of optimization techniques. Models were proposed for solving this problem using linearly constrained optimization and unconstrained optimization. However, the optical parameters of “very thin” fi...

This paper proposes an unconstrained optimization approach to the design and upgrade of sensor networks. The proposition is motivated by the fact that current methods require threshold values for different instrumentation network attributes, which are in turn very difficult to determine. In fact, these attributes are, after all, related to the process economics and therefore should not be arbit...

Discovering and utilizing problem domain knowledge is a promising direction towards improving the efficiency of evolutionary algorithms (EAs) when solving optimization problems. We propose a knowledge-based variable reduction strategy (VRS) that can be integrated into EAs to solve unconstrained and first-order derivative optimization functions more efficiently. VRS originates from the knowledge...

In this paper, we introduce a numerical method to determine the stability set of the first kind for fuzzy multi-objective non-linear programming (FMONP) problem. An α−level set of the fuzzy vector is used together with a weighting approach to transform (FMONP) problem to a single-objective inequality constrained optimization (SOICO) problem. An active set strategy is used together with a multip...

We consider the generalized Nash equilibrium problem which, in contrast to the standard Nash equilibrium problem, allows joint constraints of all players involved in the game. Using a regularized Nikaido-Isoda-function, we then present three optimization problems related to the generalized Nash equilibrium problem. The first optimization problem is a complete reformulation of the generalized Na...