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A step size rule for unconstrained optimization
Computing, 1993This short paper proposes an alternative rule for choosing the step size in the line search associated with unconstrained nonlinear minimization of \(f(x)\), \(x\in \mathbb{R}^ n\). Given a descent direction \(d_ i\), the usual line search phase is: find \(\alpha_ i\geq 0\) satisfying \(f(x_ i,\alpha_ id_ i)=min\{f(x_ i+\alpha d_ i), \alpha\geq 0 ...
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A homogeneous method for unconstrained optimization
Journal of Interdisciplinary Mathematics, 2004Abstract 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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Parallel algorithms for unconstrained optimization
1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, 1979In 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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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 mainopenaire +2 more sources