Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results.

Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results.

An Adaptive Regularisation algorithm using Cubics (ARC) is proposed for unconstrained optimization, generalizing at the same time an unpublished method due to Griewank (Technical Report NA/12, 1981, DAMTP, University of Cambridge), an algorithm by Nesterov and Polyak (Math Program 108(1):177-205, 20...

Ausführliche Beschreibung

Bibliographische Detailangaben
Hauptverfasser:	Cartis, C, Gould, N, Toint, P
Format:	Journal article
Sprache:	English
Veröffentlicht:	2011

Ähnliche Einträge

Adaptive cubic regularisation methods for unconstrained optimization. Part II: worst-case function- and derivative-evaluation complexity.
von: Cartis, C, et al.
Veröffentlicht: (2011)

Adaptive cubic overestimation methods for unconstrained optimization
von: Cartis, C, et al.
Veröffentlicht: (2007)

Adaptive cubic overestimation methods for unconstrained optimization
von: Cartis, C, et al.
Veröffentlicht: (2007)

Evaluation complexity of adaptive cubic regularization methods for convex unconstrained optimization.
von: Cartis, C, et al.
Veröffentlicht: (2012)

Complexity bounds for second-order optimality in unconstrained optimization.
von: Cartis, C, et al.
Veröffentlicht: (2012)

Trust-region and other regularisations of linear least-squares problems
von: Cartis, C, et al.
Veröffentlicht: (2008)

Trust-region and other regularisations of linear least-squares problems
von: Cartis, C, et al.
Veröffentlicht: (2009)

On the Complexity of Steepest Descent, Newton's and Regularized Newton's Methods for Nonconvex Unconstrained Optimization Problems.
von: Cartis, C, et al.
Veröffentlicht: (2010)

Worst-case evaluation complexity of regularization methods for smooth unconstrained optimization using Hölder continuous gradients
von: Cartis, C, et al.
Veröffentlicht: (2017)

An adaptive cubic regularization algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity
von: Cartis, C, et al.
Veröffentlicht: (2012)

A concise second-order complexity analysis for unconstrained optimization using high-order regularized models
von: Cartis, C, et al.
Veröffentlicht: (2019)

On the evaluation complexity of cubic regularization methods for potentially rank-deficient nonlinear least-squares problems and its relevance to constrained nonlinear optimization
von: Cartis, C, et al.
Veröffentlicht: (2013)

Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
von: Cartis, C, et al.
Veröffentlicht: (2017)

Worst-case evaluation complexity and optimality of second-order methods for nonconvex smooth optimization
von: Cartis, C, et al.
Veröffentlicht: (2018)

Convergence of a Regularized Euclidean Residual Algorithm for Nonlinear Least-Squares.
von: Bellavia, S, et al.
Veröffentlicht: (2010)

Second-order optimality and beyond: characterization and evaluation complexity in convexly-constrained nonlinear optimization
von: Cartis, C, et al.
Veröffentlicht: (2017)

On the evaluation complexity of constrained nonlinear least-squares and general constrained nonlinear optimization using second-order methods
von: Cartis, C, et al.
Veröffentlicht: (2015)

Numerical methods for unconstrained optimization/
von: Murray, Walter
Veröffentlicht: (1972)

On the complexity of finding first-order critical points in constrained nonlinear optimization
von: Cartis, C, et al.
Veröffentlicht: (2012)

Corrigendum: On the complexity of finding first-order critical points in constrained nonlinear optimization
von: Cartis, C, et al.
Veröffentlicht: (2016)

Sharp worst-case evaluation complexity bounds for arbitrary-order nonconvex optimization with inexpensive constraints
von: Cartis, C, et al.
Veröffentlicht: (2020)

On solving trust−region and other regularised subproblems in optimization
von: Gould, H, et al.
Veröffentlicht: (2009)

Evaluation complexity bounds for smooth constrained nonlinear optimization using scaled KKT conditions and high-order models
von: Cartis, C, et al.
Veröffentlicht: (2019)

Convergence and stability of line search methods for unconstrained optimization.
von: Leong, Wah June, et al.
Veröffentlicht: (2013)

On solving trust-region and other regularised subproblems in optimization
von: Dollar, H, et al.
Veröffentlicht: (2009)

Numerical methods for unconstrained optimization : an introduction/
von: 389147 Wolfe, Michael Anthony
Veröffentlicht: (1978)

Universal regularization methods: varying the power, the smoothness and the accuracy
von: Cartis, C, et al.
Veröffentlicht: (2019)

Global convergence of conjugate gradient method in unconstrained optimization problems
von: Huda Younus Najm, et al.
Veröffentlicht: (2019-10-01)

Adaptive regularization with cubics on manifolds
von: Agarwal, N, et al.
Veröffentlicht: (2020)

Numerical methods for unconstrained optimization and nonlinear equations/
von: 352576 Dennis, J. E., et al.
Veröffentlicht: (1983)

A dimensionality reduction technique for unconstrained global optimization of functions with low effective dimensionality
von: Cartis, C, et al.
Veröffentlicht: (2021)

Global Convergence of a Spectral Conjugate Gradient Method for Unconstrained Optimization
von: Jinkui Liu, et al.
Veröffentlicht: (2012-01-01)

On the Evaluation Complexity of Composite Function Minimization with Applications to Nonconvex Nonlinear Programming.
von: Cartis, C, et al.
Veröffentlicht: (2011)

On the Oracle Complexity of First-Order and Derivative-Free Algorithms for Smooth Nonconvex Minimization.
von: Cartis, C, et al.
Veröffentlicht: (2012)

A note about the complexity of minimizing Nesterov's smooth Chebyshev-Rosenbrock function.
von: Cartis, C, et al.
Veröffentlicht: (2013)

Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems
von: Din, Fakhrud, et al.
Veröffentlicht: (2022)

The Global Convergence of a New Mixed Conjugate Gradient Method for Unconstrained Optimization
von: Yang Yueting, et al.
Veröffentlicht: (2012-01-01)

A Modified Conjugate Gradient Method with Global Convergence Property for Unconstrained Optimization
von: Huda Issam Ahmed, et al.
Veröffentlicht: (2013-12-01)

An Improved Adaptive Trust-Region Method for Unconstrained Optimization
von: Hamid Esmaeili, et al.
Veröffentlicht: (2014-09-01)

Adaptive Cuckoo Search Algorithm for Unconstrained Optimization
von: Pauline Ong
Veröffentlicht: (2014-01-01)