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A new accelerated conjugate gradient method for large-scale unconstrained optimization. (English) Zbl 1499.90216

Summary: In this paper, we present a new conjugate gradient method using an acceleration scheme for solving large-scale unconstrained optimization. The generated search direction satisfies both the sufficient descent condition and the Dai-Liao conjugacy condition independent of line search. Moreover, the value of the parameter contains more useful information without adding more computational cost and storage requirements, which can improve the numerical performance. Under proper assumptions, the global convergence result of the proposed method with a Wolfe line search is established. Numerical experiments show that the given method is competitive for unconstrained optimization problems, with a maximum dimension of 100,000.

MSC:

90C30 Nonlinear programming
65K05 Numerical mathematical programming methods
90C52 Methods of reduced gradient type
90C53 Methods of quasi-Newton type
90C06 Large-scale problems in mathematical programming

Software:

CGOPT

References:

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