Results 291 to 300 of about 111,305 (313)

A method of unconstrained global optimization

Mathematical Biosciences, 1970
Abstract The method that is defined in the following finds the maximum or minimum of a real-valued function of many variables even if the function has local maxima or minima. The methods is iterative and guaranteed to converge for polynomials in several variables up to fourth degree. It can also be used successfully for other types of functions.
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Constrained and Unconstrained Propeller Blade Optimization

Journal of Aircraft, 2014
Propeller optimization by means of calculus of variations is addressed in this paper. A procedure for deriving the Euler–Lagrange equations for both unconstrained and constrained propeller blade-twist optimization is presented. This same procedure can be followed to optimize other geometric parameters, but blade twist is considered here to investigate ...
Dorfling, Johann, Rokhsaz, Kamran
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Nonlinear complementarity as unconstrained optimization

Journal of Optimization Theory and Applications, 1996
Several methods for solving the nonlinear complementarity problem (NCP) are developed. These methods are generalizations of the recently proposed algorithms of Mangasarian and Solodov (Ref. 1) and are based on an unconstrianed minimization formulation of the nonlinear complementarity problem.
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A homogeneous method for unconstrained optimization

Journal of Interdisciplinary Mathematics, 2004
Abstract A new algorithm for function minimization is presented. This algorithm is based upon homogenous functions. Consequently a (n + 2) step convergence is obtained for homogenous functions on (n) variables, while evaluation or estimation of the Hessian matrix is not needed.
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Unconstrained Optimization on Riemannian Manifolds

2021
In this chapter, we introduce the concept of a Riemannian manifold, based on which Riemannian optimization is developed. We also introduce the concept of a retraction, which is important when searching for the next point in an optimization procedure.
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Parallel algorithms for unconstrained optimization

1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, 1979
In this paper, parallel algorithms are proposed for locating the minimum point of a strictly convex quadratic function. The proposed algorithms are based on a new idea of group conjugacy and they can be considered parallel extensions of conjugate direction methods.
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Theory of algorithms for unconstrained optimization

Acta Numerica, 1992
A few months ago, while preparing a lecture to an audience that included engineers and numerical analysts, I asked myself the question: from the point of view of a user of nonlinear optimization routines, how interesting and practical is the body of theoretical analysis developed in this field?
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Large scale unconstrained optimization

Computers & Chemical Engineering, 1983
Abstract This paper concerns recent developments in methods for minimizing unconstrained nonlinear functions in many variables. Recent theoretical results and computational experience with truncated Newton methods and Buckley's variable storage preconditioned conjugate gradient method are stressed.
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Unconstrained Optimization

2022
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Large-Scale Unconstrained Optimization

This work is a survey on the methods for large scale unconstrained optimization. Besides its own theoretical importance, the growing interest in the last years in solving problems with a larger and larger number of variables are arising very frequently from real world as a result of modeling systems with a very complex structure. In this paper the main
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