3,512 research outputs found
Multipoint secant and interpolation methods with nonmonotone line search for solving systems of nonlinear equations
Multipoint secant and interpolation methods are effective tools for solving
systems of nonlinear equations. They use quasi-Newton updates for approximating
the Jacobian matrix. Owing to their ability to more completely utilize the
information about the Jacobian matrix gathered at the previous iterations,
these methods are especially efficient in the case of expensive functions. They
are known to be local and superlinearly convergent. We combine these methods
with the nonmonotone line search proposed by Li and Fukushima (2000), and study
global and superlinear convergence of this combination. Results of numerical
experiments are presented. They indicate that the multipoint secant and
interpolation methods tend to be more robust and efficient than Broyden's
method globalized in the same way
Probabilistic Interpretation of Linear Solvers
This manuscript proposes a probabilistic framework for algorithms that
iteratively solve unconstrained linear problems with positive definite
for . The goal is to replace the point estimates returned by existing
methods with a Gaussian posterior belief over the elements of the inverse of
, which can be used to estimate errors. Recent probabilistic interpretations
of the secant family of quasi-Newton optimization algorithms are extended.
Combined with properties of the conjugate gradient algorithm, this leads to
uncertainty-calibrated methods with very limited cost overhead over conjugate
gradients, a self-contained novel interpretation of the quasi-Newton and
conjugate gradient algorithms, and a foundation for new nonlinear optimization
methods.Comment: final version, in press at SIAM J Optimizatio
Composing Scalable Nonlinear Algebraic Solvers
Most efficient linear solvers use composable algorithmic components, with the
most common model being the combination of a Krylov accelerator and one or more
preconditioners. A similar set of concepts may be used for nonlinear algebraic
systems, where nonlinear composition of different nonlinear solvers may
significantly improve the time to solution. We describe the basic concepts of
nonlinear composition and preconditioning and present a number of solvers
applicable to nonlinear partial differential equations. We have developed a
software framework in order to easily explore the possible combinations of
solvers. We show that the performance gains from using composed solvers can be
substantial compared with gains from standard Newton-Krylov methods.Comment: 29 pages, 14 figures, 13 table
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