May 22, 2015 · The algorithm was proposed as a strategy for establishing global convergence guarantees when solving nonconvex, locally Lipschitz optimization ...
Mar 30, 2015 · The algorithm was proposed as a strategy for establishing global convergence guarantees when solving nonconvex, locally. Lipschitz optimization ...
Sep 30, 2024 · Abstract: A line search algorithm for minimizing nonconvex and/or nonsmooth objective functions is presented. The algorithm is a hybrid ...
Under suitable assumptions, it is proved that the algorithm converges globally with probability one and the results of numerical experiments illustrate the ...
Dec 30, 2016 · Under suitable assumptions, it is proved that the algorithm converges globally with probability one. The algorithm has been implemented in C++ ...
A Quasi-Newton Algorithm for Nonconvex, Nonsmooth Optimization with Global Convergence Guarantees. Mathematical Programming Computation, 7(4):399–428, 2015 ...
TL;DR: Under suitable assumptions, it is proved that the algorithm converges globally with probability one and the results of numerical experiments illustrate ...
Feb 16, 2023 · In this paper, we close this gap and present the first globally convergent quasi-Newton method with an explicit non-asymptotic superlinear convergence rate.
Missing: nonsmooth | Show results with:nonsmooth
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Gradient sampling (GS) has proved to be an effective methodology for the minimization of objective functions that may be nonconvex and/or nonsmooth.
This work extends the well-known BFGS quasi-Newton method and its limited-memory variant LBFGS to the optimization of non-smooth convex objectives by ...