979 research outputs found
Forward-backward truncated Newton methods for convex composite optimization
This paper proposes two proximal Newton-CG methods for convex nonsmooth
optimization problems in composite form. The algorithms are based on a a
reformulation of the original nonsmooth problem as the unconstrained
minimization of a continuously differentiable function, namely the
forward-backward envelope (FBE). The first algorithm is based on a standard
line search strategy, whereas the second one combines the global efficiency
estimates of the corresponding first-order methods, while achieving fast
asymptotic convergence rates. Furthermore, they are computationally attractive
since each Newton iteration requires the approximate solution of a linear
system of usually small dimension
Splitting methods with variable metric for KL functions
We study the convergence of general abstract descent methods applied to a
lower semicontinuous nonconvex function f that satisfies the
Kurdyka-Lojasiewicz inequality in a Hilbert space. We prove that any precompact
sequence converges to a critical point of f and obtain new convergence rates
both for the values and the iterates. The analysis covers alternating versions
of the forward-backward method with variable metric and relative errors. As an
example, a nonsmooth and nonconvex version of the Levenberg-Marquardt algorithm
is detailled
Globally Convergent Coderivative-Based Generalized Newton Methods in Nonsmooth Optimization
This paper proposes and justifies two globally convergent Newton-type methods
to solve unconstrained and constrained problems of nonsmooth optimization by
using tools of variational analysis and generalized differentiation. Both
methods are coderivative-based and employ generalized Hessians (coderivatives
of subgradient mappings) associated with objective functions, which are either
of class , or are represented in the form of convex
composite optimization, where one of the terms may be extended-real-valued. The
proposed globally convergent algorithms are of two types. The first one extends
the damped Newton method and requires positive-definiteness of the generalized
Hessians for its well-posedness and efficient performance, while the other
algorithm is of {the regularized Newton type} being well-defined when the
generalized Hessians are merely positive-semidefinite. The obtained convergence
rates for both methods are at least linear, but become superlinear under the
semismooth property of subgradient mappings. Problems of convex composite
optimization are investigated with and without the strong convexity assumption
{on smooth parts} of objective functions by implementing the machinery of
forward-backward envelopes. Numerical experiments are conducted for Lasso
problems and for box constrained quadratic programs with providing performance
comparisons of the new algorithms and some other first-order and second-order
methods that are highly recognized in nonsmooth optimization.Comment: arXiv admin note: text overlap with arXiv:2101.1055
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