99 research outputs found
Generalized Newton's Method based on Graphical Derivatives
This paper concerns developing a numerical method of the Newton type to solve
systems of nonlinear equations described by nonsmooth continuous functions. We
propose and justify a new generalized Newton algorithm based on graphical
derivatives, which have never been used to derive a Newton-type method for
solving nonsmooth equations. Based on advanced techniques of variational
analysis and generalized differentiation, we establish the well-posedness of
the algorithm, its local superlinear convergence, and its global convergence of
the Kantorovich type. Our convergence results hold with no semismoothness
assumption, which is illustrated by examples. The algorithm and main results
obtained in the paper are compared with well-recognized semismooth and
-differentiable versions of Newton's method for nonsmooth Lipschitzian
equations
Deflation for semismooth equations
Variational inequalities can in general support distinct solutions. In this
paper we study an algorithm for computing distinct solutions of a variational
inequality, without varying the initial guess supplied to the solver. The
central idea is the combination of a semismooth Newton method with a deflation
operator that eliminates known solutions from consideration. Given one root of
a semismooth residual, deflation constructs a new problem for which a
semismooth Newton method will not converge to the known root, even from the
same initial guess. This enables the discovery of other roots. We prove the
effectiveness of the deflation technique under the same assumptions that
guarantee locally superlinear convergence of a semismooth Newton method. We
demonstrate its utility on various finite- and infinite-dimensional examples
drawn from constrained optimization, game theory, economics and solid
mechanics.Comment: 24 pages, 3 figure
A squared smoothing Newton method for nonsmooth matrix equations and its applications in semidefinite optimization problems
10.1137/S1052623400379620SIAM Journal on Optimization143783-80
Optimization and Applications
Proceedings of a workshop devoted to optimization problems, their theory and resolution, and above all applications of them. The topics covered existence and stability of solutions; design, analysis, development and implementation of algorithms; applications in mechanics, telecommunications, medicine, operations research
A trust region-type normal map-based semismooth Newton method for nonsmooth nonconvex composite optimization
We propose a novel trust region method for solving a class of nonsmooth and
nonconvex composite-type optimization problems. The approach embeds inexact
semismooth Newton steps for finding zeros of a normal map-based stationarity
measure for the problem in a trust region framework. Based on a new merit
function and acceptance mechanism, global convergence and transition to fast
local q-superlinear convergence are established under standard conditions. In
addition, we verify that the proposed trust region globalization is compatible
with the Kurdyka-{\L}ojasiewicz (KL) inequality yielding finer convergence
results. We further derive new normal map-based representations of the
associated second-order optimality conditions that have direct connections to
the local assumptions required for fast convergence. Finally, we study the
behavior of our algorithm when the Hessian matrix of the smooth part of the
objective function is approximated by BFGS updates. We successfully link the KL
theory, properties of the BFGS approximations, and a Dennis-Mor{\'e}-type
condition to show superlinear convergence of the quasi-Newton version of our
method. Numerical experiments on sparse logistic regression and image
compression illustrate the efficiency of the proposed algorithm.Comment: 56 page
Variational Properties of Decomposable Functions Part II: Strong Second-Order Theory
Local superlinear convergence of the semismooth Newton method usually
requires the uniform invertibility of the generalized Jacobian matrix, e.g.
BD-regularity or CD-regularity. For several types of nonlinear programming and
composite-type optimization problems -- for which the generalized Jacobian of
the stationary equation can be calculated explicitly -- this is characterized
by the strong second-order sufficient condition. However, general
characterizations are still not well understood. In this paper, we propose a
strong second-order sufficient condition (SSOSC) for composite problems whose
nonsmooth part has a generalized conic-quadratic second subderivative. We then
discuss the relationship between the SSOSC and another second order-type
condition that involves the generalized Jacobians of the normal map. In
particular, these two conditions are equivalent under certain structural
assumptions on the generalized Jacobian matrix of the proximity operator. Next,
we verify these structural assumptions for -strictly decomposable
functions via analyzing their second-order variational properties under
additional geometric assumptions on the support set of the decomposition pair.
Finally, we show that the SSOSC is further equivalent to the strong metric
regularity condition of the subdifferential, the normal map, and the natural
residual. Counterexamples illustrate the necessity of our assumptions.Comment: 28 pages; preliminary draf
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