Combination of steepest descent and BFGS methods for nonconvex nonsmooth optimization
From MaRDI portal
Publication:285034
DOI10.1007/s11075-015-0034-2zbMath1338.49071OpenAlexW1169901681MaRDI QIDQ285034
Publication date: 18 May 2016
Published in: Numerical Algorithms (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11075-015-0034-2
Lipschitz functionsWolfe conditionsnonconvex nonsmooth optimizationnonsmooth BFGS methodnonsmooth line search method
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (4)
A new descent algorithm using the three-step discretization method for solving unconstrained optimization problems ⋮ An extension of the quasi-Newton method for minimizing locally Lipschitz functions ⋮ Line Search Algorithms for Locally Lipschitz Functions on Riemannian Manifolds ⋮ An SQP method for minimization of locally Lipschitz functions with nonlinear constraints
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Algorithms for finite and semi-infinite Min-Max-Min problems using adaptive smoothing techniques
- Globally convergent limited memory bundle method for large-scale nonsmooth optimization
- Discrete gradient method: Derivative-free method for nonsmooth optimization
- Computing proximal points of nonconvex functions
- Nondifferential optimization via adaptive smoothing
- An efficient line search for nonlinear least squares
- A bundle-Newton method for nonsmooth unconstrained minimization
- Proximal quasi-Newton methods for nondifferentiable convex optimization
- Variable metric bundle methods: From conceptual to implementable forms
- Continuous subdifferential approximations and their applications
- On the superlinear convergence of the variable metric proximal point algorithm using Broyden and BFGS matrix secant updating
- A family of variable metric proximal methods
- Globally convergent variable metric method for convex nonsmooth unconstrained minimization
- A quasi-second-order proximal bundle algorithm
- An effective nonsmooth optimization algorithm for locally Lipschitz functions
- Methods of descent for nondifferentiable optimization
- Piecewise linear approximations in nonconvex nonsmooth optimization
- A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning
- Limited memory bundle method for large bound constrained nonsmooth optimization: convergence analysis
- Convergence of the Gradient Sampling Algorithm for Nonsmooth Nonconvex Optimization
- Finding the nearest point in A polytope
- Optimization of lipschitz continuous functions
- An Algorithm for Constrained Optimization with Semismooth Functions
- Line search algorithms with guaranteed sufficient decrease
- Quasi-Newton Bundle-Type Methods for Nondifferentiable Convex Optimization
- Finding the Point of a Polyhedron Closest to the Origin
- Minimizing Nonconvex Nonsmooth Functions via Cutting Planes and Proximity Control
- A DC piecewise affine model and a bundling technique in nonconvex nonsmooth minimization
- A Globally and Superlinearly Convergent Algorithm for Nonsmooth Convex Minimization
- Generalized Bundle Methods
- Asymptotic Convergence Analysis of Some Inexact Proximal Point Algorithms for Minimization
- A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization
- A Descent Numerical Method for Optimization Problems with Nondifferentiable Cost Functionals
- Gobally convergent variable metric method for nonconvex nondifferentiable unconstrained minimization
- Benchmarking optimization software with performance profiles.
This page was built for publication: Combination of steepest descent and BFGS methods for nonconvex nonsmooth optimization
