1,007 research outputs found
Exploiting damped techniques for nonlinear conjugate gradient methods
In this paper we propose the use of damped techniques within Nonlinear Conjugate Gradient (NCG) methods. Damped techniques were introduced by Powell and recently reproposed by Al-Baali and till now, only applied in the framework of quasi–Newton methods. We extend their use to NCG methods in large scale unconstrained optimization, aiming at possibly improving the efficiency and the robustness of the latter methods, especially when solving difficult problems. We consider both unpreconditioned and Preconditioned NCG (PNCG). In the latter case, we embed damped techniques within a class of preconditioners based on quasi–Newton updates. Our purpose is to possibly provide efficient preconditioners which approximate, in some sense, the inverse of the Hessian matrix, while still preserving information provided by the secant equation or some of its modifications. The results of an extensive numerical experience highlights that the proposed approach is quite promising
Stochastic quasi-Newton molecular simulations
Article / Letter to editorLeiden Institute of Chemistr
Enhancing structure relaxations for first-principles codes: an approximate Hessian approach
We present a method for improving the speed of geometry relaxation by using a
harmonic approximation for the interaction potential between nearest neighbor
atoms to construct an initial Hessian estimate. The model is quite robust, and
yields approximately a 30% or better reduction in the number of calculations
compared to an optimized diagonal initialization. Convergence with this
initializer approaches the speed of a converged BFGS Hessian, therefore it is
close to the best that can be achieved. Hessian preconditioning is discussed,
and it is found that a compromise between an average condition number and a
narrow distribution in eigenvalues produces the best optimization.Comment: 9 pages, 3 figures, added references, expanded optimization sectio
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