Showing papers on "Continuous optimization published in 1983"

Optimization by Simulated Annealing

Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

Global optimization by controlled random search

Abstract: The paper describes a new version, known as CRS2, of the author's controlled random search procedure for global optimization (CRS). The new procedure is simpler and requires less computer storage than the original version, yet it has a comparable performance. The results of comparative trials of the two procedures, using a set of standard test problems, are given. These test problems are examples of unconstrained optimization. The controlled random search procedure can also be effective in the presence of constraints. The technique of constrained optimization using CRS is illustrated by means of examples taken from the field of electrical engineering.

Network Optimization with Continuous Control Parameters

Abstract: In this paper we consider two network optimization problems which have the following characteristics: control parameters vary continuously and network users behave according to War-drop's first principle of traffic equilibrium (“user-optimization”). For each problem, we study an exact algorithm based on constraint accumulation and a heuristic algorithm previously proposed in the literature.

Solution of a statistical optimization problem by rearrangement methods

Abstract: Inequalities for the rearrangement of functions are applied to obtain a solution of a statistical optimization problem. This optimization problem arises in situations where one wants to describe the influence of stochastic dependence on a statistical problem.

Improved infeasible path optimization for sequential modular simulators.

Abstract: Abstract Recently, it was shown that chemical processes modeled by steady-state simulators could be optimized without repeatedly converging the process simulation. Instead, optimization and simulation of the process can be performed simultaneously (along an infeasible path), thus leading to much more efficient performance. In this two-part study, several improvements to this infeasible path approach are described. This first paper deals with improvements to the interface between the optimization algorithm and the process simulator. Here one is primarily concerned with obtaining the necessary function and gradient information for the optimization with minimum computational effort from the process simulator. The architecture of sequential modular simulators, the structure of process optimization problems and any sources of error in obtaining the necessary gradient information for the optimization algorithm are considered. To this end, a chainruling algorithm is derived that allows the incorporation of analytic derivative information for parts of the flowsheet and generally leads to less frequent evaluation of the flowsheet modules. This algorithm is demonstrated on three process optimization problems. The results indicate significant improvement in performance.

The Single-Cycle Scheme: A New Approach to Numerical Optimization

Abstract: A new scheme is presented for solving optimization problems, in which the objective function is dependent on the solution of a partial differential equation. The scheme can be obtained by modifying any standard iterative procedure for solving the partial-differential equation. This modified procedure, which updates the solution of the differential equation and the design parameters simultaneously, eliminates the need for the costly inner-outer iterative procedure. The scheme is demonstrated by application to the problem of determining wind tunnel wall interference corrections. Results indicate that the ratio of the cost of solving the optimization problem to the cost of solving the partial-differential equation using a standard iterative scheme is less than L +1, where L is the number of design parameters.

Identification of continuous time rational expectations models from discrete time data

Abstract: This paper shows how the cross-equation restrictions implied by dynamic rational expectations models can be used to resolve the aliasing identification problem. Using a continuous time, linear-quadratic optimization environment, this paper describes how the resulting restrictions are sufficient to identify the parameters of the underlying continuous time process when it is known that the true continuous time process has a rational spectral density matrix.

Vector Optimization Techniques.

Abstract: : Multiple Objective Optimization Theory (MOOT) techniques are receiving increasing attention due to their ability to incorporate salient non-commensurate and conflicting objectives of an analysis or design situation into the choice making process A common implementation of MOOT is by way of a vector optimization process Vector Optimization is used for generating optimum solutions for alternatives which extremize the components of a vectors of objective functions or performance indices The weighting anc constraint techniques are presented as ways of practically implementing an optimization process for a vectors of cost functions to generate a Pareto optimal or non-dominated solution set Computer programs are discussed which accomplish the vectors optimization process for the parameter optimization class of linear problems (MOOTLP) and non-linear problems (PROCES) A bibliographic summary of recent vector optimization efforts is included (Author)

Convergence of Dual Variables in Interactive Vector Optimization

Abstract: This paper is concerned with the problem of finding vector optima through an iterative procedure of an interactive type in a differentiable framework. The optima are defined according to a partial ordering on the different objective functions induced by a cone and the procedure can be viewed as an extension of the gradient method used in scalar optimization. The paper is focused on establishing results of convergence both for primal and dual variables. One of the characteristics of the procedure described in this paper is the possibility of controlling the iteration through the dual variables.