A Mixed Hybrid Conjugate Gradient Method for Unconstrained Engineering Optimization Problems

Ezugwu

Stat., optim. inf. comput.

2020

The conjugate gradient method is a very efficient iterative technique for solving large-scale unconstrainedoptimization problems. Motivated by recent modifications of some variants of the method and construction of hybrid methods, this study proposed four hybrid methods that are globally convergent as well as computationally efficient. The approach adopted for constructing the hybrid methods entails projecting ten recently modified conjugate gradient methods. Each of the hybrid methods is shown to satisfy the descent property independent of any line search technique and globally convergent under the influence of strong Wolfe line search. Results obtained from numerical implementation of these methods and performance profiling show that the methods are very competitive with well-known traditional methods.

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“…The descent property is satisfied in ( 23)- (26). Hence, the sequence of search directions generated by the DPH method satisfies the descent property.…”

Section: The Second Assumption In This Case Allows Us To Set Gmentioning

confidence: 96%

“…where c is a positive constant. For more information on the theories and applications of CG methods, we refer the reader to [20,21,22,23,25,26,32]. Each CG method has very striking features that makes it adaptable to some sets of unconstrained problems.…”

mentioning

confidence: 99%

A New Family of Hybrid Conjugate Gradient Methods for Unconstrained Optimization

Ezugwu

Stat., optim. inf. comput.

2020

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A PRP-HS Type Hybrid Nonlinear Conjugate Gradient Method for Solving Unconstrained Optimization Problems

Olusanya

Intelligent Systems Applications in Software Engineering

2019

A Globally Convergent Hybrid FR-PRP Conjugate Gradient Method for Unconstrained Optimization Problems

Wseas Transactions on Mathematics

Raji³

2022