Nonmonotone spectral gradient method based on memoryless symmetric rank-one update for large-scale unconstrained optimization
This paper proposes a nonmonotone spectral gradient method for solving large-scale unconstrained optimization problems. The spectral parameter is derived from the eigenvalues of an optimally sized memoryless symmetric rank-one matrix obtained under the measure defined as a ratio of the determinant...
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| Format: | Article |
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American Institute of Mathematical Sciences
2021
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