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Title of Journal: Comput Optim Appl

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Abbravation: Computational Optimization and Applications

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Springer US

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10.1016/0022-5193(81)90106-5

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1573-2894

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Multistep nonlinear conjugate gradient methods fo

Authors: John A Ford Yasushi Narushima Hiroshi Yabe
Publish Date: 2007/11/09
Volume: 40, Issue: 2, Pages: 191-216
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Abstract

Conjugate gradient methods are appealing for large scale nonlinear optimization problems because they avoid the storage of matrices Recently seeking fast convergence of these methods Dai and Liao Appl Math Optim 4387–101 2001 proposed a conjugate gradient method based on the secant condition of quasiNewton methods and later Yabe and Takano Comput Optim Appl 28203–225 2004 proposed another conjugate gradient method based on the modified secant condition In this paper we make use of a multistep secant condition given by Ford and Moghrabi Optim Methods Softw 2357–370 1993 J Comput Appl Math 50305–323 1994 and propose two new conjugate gradient methods based on this condition The methods are shown to be globally convergent under certain assumptions Numerical results are reported

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  1. A benchmark library and a comparison of heuristic methods for the linear ordering problem
  2. Compression and denoising using l 0 -norm
  3. AAR-based decomposition algorithm for non-linear convex optimisation
  4. Classification of companies using maximal margin ellipsoidal surfaces
  5. Optimization-based design of plant-friendly multisine signals using geometric discrepancy criteria
  6. Minimization of the Tikhonov functional in Banach spaces smooth and convex of power type by steepest descent in the dual
  7. Numerical experiments with an inexact Jacobian trust-region algorithm
  8. An interior-point approach for primal block-angular problems
  9. Computing the metric dimension of graphs by genetic algorithms
  10. Some non-interior path-following methods based on a scaled central path for linear complementarity problems
  11. On the resolution of certain discrete univariate max–min problems
  12. A coordinate gradient descent method for ℓ 1 -regularized convex minimization
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