A new globalization technique for nonlinear conjugate gradient methods for nonconvex minimization

DOI10.1016/j.amc.2011.05.032zbMath1232.65096OpenAlexW2171032533MaRDI QIDQ555476

Li Zhang, Jun-Li Li

Publication date: 22 July 2011

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2011.05.032

zbMATH Keywords

global convergencesteepest descentnonlinear conjugate gradient methodLiu-Storey methodPolak-Ribiere-Polyak methodstep modification

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26)

Related Items (14)

Notes on the complete elliptic integral of the first kind ⋮ Schur convexity and inequalities for a multivariate symmetric function ⋮ Some nonlinear conjugate gradient methods with sufficient descent condition and global convergence ⋮ Dai-Kou type conjugate gradient methods with a line search only using gradient ⋮ A modified Polak-Ribière-Polyak conjugate gradient algorithm for nonsmooth convex programs ⋮ A hybrid self-adjusted mean value method for reliability-based design optimization using sufficient descent condition ⋮ A hybrid conjugate finite-step length method for robust and efficient reliability analysis ⋮ Sharp bounds for Sándor-Yang means in terms of one-parameter family of bivariate means ⋮ Petrović-type inequalities for harmonic \(h\)-convex functions ⋮ Optimal two-parameter geometric and arithmetic mean bounds for the Sándor-Yang mean ⋮ A note on generalized convex functions ⋮ Some new fractional integral inequalities for exponentially \(m\)-convex functions via extended generalized Mittag-Leffler function ⋮ Hermite-Hadamard type inequalities for co-ordinated convex and qausi-convex functions and their applications ⋮ Monotonicity properties and bounds involving the two-parameter generalized Grötzsch ring function

Uses Software

• L-BFGS
• CUTEr
• SCALCG
• CUTE

Cites Work

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• The convergence properties of some new conjugate gradient methods
• On the limited memory BFGS method for large scale optimization
• Efficient generalized conjugate gradient algorithms. I: Theory
• A modified PRP conjugate gradient method
• Optimization. Algorithms and consistent approximations
• A globally convergent version of the Polak-Ribière conjugate gradient method
• Global convergence properties of nonlinear conjugate gradient methods with modified secant condition
• Global convergence of a modified Fletcher-Reeves conjugate gradient method with Armijo-type line search
• Scaled conjugate gradient algorithms for unconstrained optimization
• A three-parameter family of nonlinear conjugate gradient methods
• A descent modified Polak–Ribière–Polyak conjugate gradient method and its global convergence
• A nonlinear conjugate gradient method based on the MBFGS secant condition
• Spectral conjugate gradient methods with sufficient descent property for large-scale unconstrained optimization
• A Tool for the Analysis of Quasi-Newton Methods with Application to Unconstrained Minimization
• Global Convergence Properties of Conjugate Gradient Methods for Optimization
• CUTE
• Line search algorithms with guaranteed sufficient decrease
• A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search
• Function minimization by conjugate gradients
• Some descent three-term conjugate gradient methods and their global convergence
• Convergence Conditions for Ascent Methods
• The conjugate gradient method in extremal problems
• Methods of conjugate gradients for solving linear systems
• A modified BFGS method and its global convergence in nonconvex minimization
• Benchmarking optimization software with performance profiles.

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