Results 261 to 270 of about 13,755 (295)

Projection method for unconstrained optimization [PDF]

Journal of Optimization Theory and Applications, 1972
A method of conjugate directions, the projection method, for solving unconstrained minimization problems is presented. Under the assumption of uniform strict convexity, the method is shown to converge to the global minimizer of the unconstrained problem and to have an (n − 1)-step superlinear rate of convergence.
Klaus Ritter, Garth P. McCormick
openaire   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Unconstrained Optimization Techniques

2021
The numerical algorithms to be developed for optimal control computation in this book are based on the control parametrization technique in conjunction with a novel time scaling transform. Essentially, an optimal control problem with its control functions being approximated by an appropriate linear combination of spline functions is reduced to an ...
Kok Lay Teo, Volker Rehbock, Bin Li, Changjun Yu   +3 more
openaire   +2 more sources

An algorithm for unconstrained optimization

International Journal of Computer Mathematics, 1992
In this note an algorithm for solving unconstrained optimization problems is presented. A convergence analysis for the method is considered too. The rate of convergence is shown to be superlinear. Numerical results are reported.
openaire   +2 more sources

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.
openaire   +3 more sources

A View of Unconstrained Optimization.

1987
Abstract : Finding the unconstrained minimizer of a function of more than one variable is an important problem with many practical applications, including data fitting, engineering design, and process control. In addition, techniques for solving unconstrained optimization problems form the basis for most methods for solving constrained optimization ...
null Robert B., Schnabel Jr., J. E. Dennis   +2 more
openaire   +2 more sources

Unconstrained Optimization Problems

2010
Unconstrained optimization methods seek a local minimum (or a local maximum) in the absence of restrictions, that is, $$f(x) \longrightarrow \min (x \in D)$$ for a real-valued function f: D → ℝ defined on a nonempty subset D of ℝ n for a given n ∈ ℕ.
Dieter Hoffmann, Wilhelm Forst
openaire   +2 more sources

Unconstrained Optimization Algorithms

2019
In this chapter, we will present the most representative algorithms for solving an optimization problem without functional constraints, that is, the problem $$ \begin{array}{lll} P: &{} \text {Min} &{} f(x) \\ &{} \text {s.t.} &{} x\in C, \end{array} $$ where \(\emptyset \ne C\subset \mathop {\mathrm {dom}}f\subset \mathbb {R}^{n}.\) We will ...
Francisco J. Aragón, Marco A. López, Margarita M. L. Rodríguez, Miguel A. Goberna   +3 more
openaire   +2 more sources

Equations and Unconstrained Optimization

2014
In this chapter, we start our discussion of Newton-type methods, which are based on the fundamental principle of linear/quadratic approximation of the problem data (or of some part of the problem data). The underlying idea is extremely important, as it serves as a foundation for numerous computationally efficient algorithms for optimization and ...
Mikhail V. Solodov, Alexey F. Izmailov
openaire   +2 more sources

Nonlinear complementarity as unconstrained optimization

Journal of Optimization Theory and Applications, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources

An incremental decomposition method for unconstrained optimization

Applied Mathematics and Computation, 2014
In this work we consider the problem of minimizing a sum of continuously differentiable functions. The vector of variables is partitioned into two blocks, and we assume that the objective function is convex with respect to a block-component. Problems with this structure arise, for instance, in machine learning.
BRAVI, LUCA, SCIANDRONE, MARCO
openaire   +2 more sources