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Globally convergent conjugate gradient algorithms. (English) Zbl 0579.90079
The paper deals with the convergence of the conjugate gradient algorithms for unconstrained minimization. A test is proposed, determining when to restart the conjugate gradient methods by using the steepest descent search direction. The test examines the angle between the search direction and the negative gradient and guarantees convergence of the algorithm to a stationary point of the objective function when the latter is twice continuously differentiable and has a bounded level set.
Reviewer: V.M.Veliov

90C30 Nonlinear programming
65K05 Numerical mathematical programming methods
49M37 Numerical methods based on nonlinear programming
Algorithm 500
Full Text: DOI
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