
doi: 10.14529/mmp220309
Summary: The spectral conjugate gradient method is an essential generalization of the conjugate gradient method, and it is also one of the effective numerical methods to solve large scale unconstrained optimization problems. We propose a new spectral Dai-Yuan (SDY) conjugate gradient method to solve nonlinear unconstrained optimization problems. The proposed method's global convergence was achieved under appropriate conditions, performing numerical testing on 65 benchmark tests to determine the effectiveness of the proposed method in comparison to other methods like the AMDYN algorithm and some other existing ones like Dai-Yuan method.
Large-scale problems in mathematical programming, УДК 517.972, УДК 519.6, глобальная конвергенция, метод сопряженных градиентов, неограниченная оптимизация, sufficient descent, достаточный спуск, global convergence, Numerical mathematical programming methods, Nonlinear programming, conjugate gradient method, unconstrained optimization, спектральный сопряженный градиент, spectral conjugate gradient
Large-scale problems in mathematical programming, УДК 517.972, УДК 519.6, глобальная конвергенция, метод сопряженных градиентов, неограниченная оптимизация, sufficient descent, достаточный спуск, global convergence, Numerical mathematical programming methods, Nonlinear programming, conjugate gradient method, unconstrained optimization, спектральный сопряженный градиент, spectral conjugate gradient
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