A self-correcting variable-metric algorithm framework for nonsmooth optimization

doi:10.1093/imanum/drz008

Title: A self-correcting variable-metric algorithm framework for nonsmooth optimization

Abstract

Abstract An algorithm framework is proposed for minimizing nonsmooth functions. The framework is variable metric in that, in each iteration, a step is computed using a symmetric positive-definite matrix whose value is updated as in a quasi-Newton scheme. However, unlike previously proposed variable-metric algorithms for minimizing nonsmooth functions, the framework exploits self-correcting properties made possible through Broyden–Fletcher–Goldfarb–Shanno-type updating. In so doing, the framework does not overly restrict the manner in which the step computation matrices are updated, yet the scheme is controlled well enough that global convergence guarantees can be established. The results of numerical experiments for a few algorithms are presented to demonstrate the self-correcting behaviours that are guaranteed by the framework.

Authors:

Curtis, Frank E. ^[1]; Robinson, Daniel P. ^[2]; Zhou, Baoyu ^[1]

Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA, USA
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA

Publication Date:: 2019-03-07

Sponsoring Org.:: USDOE

OSTI Identifier:: 1498577

Grant/Contract Number:: SC0010615; DMS-1016291; DMS-1319356; IIS-1704458

Resource Type:: Published Article

Journal Name:: IMA Journal of Numerical Analysis

Additional Journal Information:: Journal Name: IMA Journal of Numerical Analysis Journal Volume: 40 Journal Issue: 2; Journal ID: ISSN 0272-4979

Publisher:: Oxford University Press

Country of Publication:: United Kingdom

Language:: English

Citation Formats


                    Curtis, Frank E., Robinson, Daniel P., and Zhou, Baoyu. A self-correcting variable-metric algorithm framework for nonsmooth optimization.  United Kingdom: N. p., 2019. 
        Web.  doi:10.1093/imanum/drz008.

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                    Curtis, Frank E., Robinson, Daniel P., & Zhou, Baoyu. A self-correcting variable-metric algorithm framework for nonsmooth optimization.  United Kingdom.  doi:10.1093/imanum/drz008.

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                    Curtis, Frank E., Robinson, Daniel P., and Zhou, Baoyu. Thu .  
        "A self-correcting variable-metric algorithm framework for nonsmooth optimization".  United Kingdom.  doi:10.1093/imanum/drz008.

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@article{osti_1498577,

  title        = {A self-correcting variable-metric algorithm framework for nonsmooth optimization},

  author       = {Curtis, Frank E. and Robinson, Daniel P. and Zhou, Baoyu},

  abstractNote = {Abstract An algorithm framework is proposed for minimizing nonsmooth functions. The framework is variable metric in that, in each iteration, a step is computed using a symmetric positive-definite matrix whose value is updated as in a quasi-Newton scheme. However, unlike previously proposed variable-metric algorithms for minimizing nonsmooth functions, the framework exploits self-correcting properties made possible through Broyden–Fletcher–Goldfarb–Shanno-type updating. In so doing, the framework does not overly restrict the manner in which the step computation matrices are updated, yet the scheme is controlled well enough that global convergence guarantees can be established. The results of numerical experiments for a few algorithms are presented to demonstrate the self-correcting behaviours that are guaranteed by the framework.},

  doi          = {10.1093/imanum/drz008},

  journal      = {IMA Journal of Numerical Analysis},

  number       = 2,

  volume       = 40,

  place        = {United Kingdom},

  year         = {2019},

  month        = {3}

}

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Title: A self-correcting variable-metric algorithm framework for nonsmooth optimization

Abstract

Citation Formats

Proximity control in bundle methods for convex nondifferentiable minimization journal, January 1990

New variants of bundle methods journal, July 1995

Algorithms for nonlinear constraints that use lagrangian functions journal, December 1978

A Version of the Bundle Idea for Minimizing a Nonsmooth Function: Conceptual Idea, Convergence Analysis, Numerical Results journal, February 1992

The Speed of Shor's R-algorithm journal, February 2008

A family of variable-metric methods derived by variational means journal, January 1970

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A characterization of superlinear convergence and its application to quasi-Newton methods journal, May 1974

A Redistributed Proximal Bundle Method for Nonconvex Optimization journal, January 2010

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Global Convergence of a Cass of Quasi-Newton Methods on Convex Problems journal, October 1987

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A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization journal, January 2005

Restricted Step and Levenberg–Marquardt Techniques in Proximal Bundle Methods for Nonconvex Nondifferentiable Optimization journal, February 1996

An adaptive gradient sampling algorithm for non-smooth optimization journal, September 2012

Globally Convergent Variable Metric Method for Nonconvex Nondifferentiable Unconstrained Minimization journal, November 2001

A bundle-Newton method for nonsmooth unconstrained minimization journal, January 1998

Efficiency of Proximal Bundle Methods journal, March 2000

Variable Metric Method for Minimization journal, February 1991

New limited memory bundle method for large-scale nonsmooth optimization journal, December 2004

Quasi-Newton Bundle-Type Methods for Nondifferentiable Convex Optimization journal, May 1998

Globally convergent limited memory bundle method for large-scale nonsmooth optimization journal, April 2006

Local and superlinear convergence of a class of variable metric methods journal, September 1979

Linear convergence of epsilon-subgradient descent methods for a class of convex functions journal, September 1999

Convergence of the Gradient Sampling Algorithm for Nonsmooth Nonconvex Optimization journal, January 2007

A Tool for the Analysis of Quasi-Newton Methods with Application to Unconstrained Minimization journal, June 1989

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An Algorithm for Constrained Optimization with Semismooth Functions journal, May 1977

Rate of Convergence of the Bundle Method journal, March 2017

Two-Point Step Size Gradient Methods journal, January 1988

Conditioning of quasi-Newton methods for function minimization journal, September 1970

A Linearization Algorithm for Nonsmooth Minimization journal, May 1985

Variable metric methods of minimisation journal, February 1969

A Method for Solving Certain Quadratic Programming Problems Arising in Nonsmooth Optimization journal, January 1986

Nonsmooth optimization via quasi-Newton methods journal, February 2012

The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations journal, January 1970

A new approach to variable metric algorithms journal, March 1970

A Trust Region Spectral Bundle Method for Nonconvex Eigenvalue Optimization journal, January 2008

A -algorithm for convex minimization journal, July 2005

Proximity control in bundle methods for convex nondifferentiable minimization
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New variants of bundle methods
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Algorithms for nonlinear constraints that use lagrangian functions
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A Version of the Bundle Idea for Minimizing a Nonsmooth Function: Conceptual Idea, Convergence Analysis, Numerical Results
journal, February 1992

The Speed of Shor's R-algorithm
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A family of variable-metric methods derived by variational means
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A characterization of superlinear convergence and its application to quasi-Newton methods
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A Redistributed Proximal Bundle Method for Nonconvex Optimization
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A family of variable metric proximal methods
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Global Convergence of a Cass of Quasi-Newton Methods on Convex Problems
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An adaptive gradient sampling algorithm for non-smooth optimization
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Quasi-Newton Bundle-Type Methods for Nondifferentiable Convex Optimization
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Globally convergent limited memory bundle method for large-scale nonsmooth optimization
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Local and superlinear convergence of a class of variable metric methods
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A Tool for the Analysis of Quasi-Newton Methods with Application to Unconstrained Minimization
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An Algorithm for Constrained Optimization with Semismooth Functions
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Conditioning of quasi-Newton methods for function minimization
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A Linearization Algorithm for Nonsmooth Minimization
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A Method for Solving Certain Quadratic Programming Problems Arising in Nonsmooth Optimization
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