Mathematics of Computation · 1967 · 123 citations · 6 references
Natural SciencesComputer ScienceMathematicsAlgorithmsOptimizationTheory Of ComputationConvex AnalysisConvex OptimizationGradient-based OptimizationNonlinear ProgrammingNumerical AlgorithmsOptimization TheoryEfficiencyGradient DescentGradient ProgrammingQuadratic OptimizationSteepest Descent
The paper compares gradient methods for function maximization using Crockett and Chernoff’s characterization of the class. Efficiency is defined for a gradient step, enabling comparison with Newton’s method, and a simple correction for non‑concave functions is proposed. For quadratic functions, Newton’s method is the most efficient, while for non‑concave functions the Newton direction can be the opposite of the optimal direction.
A comparison is made among various gradient methods for maximizing a function, based on a characterization by Crockett and Chernoff of the class of these methods. By defining the “efficiency” of a gradient step in a certain way, it becomes easy to compare the efficiencies of different schemes with that of Newton’s method, which can be regarded as a particular gradient scheme. For quadratic functions, it is shown that Newton’s method is the most efficient (a conclusion which may be approximately true for nonquadratic functions). For functions which are not concave (downward), it is shown that the Newton direction may be just the opposite of the most desirable one. A simple way of correcting this is explained.
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A Rapidly Convergent Descent Method for Minimization
R. Fletcher, M. J. D. Powell · The Computer Journal · 1963 · 4.6K citations · Full text
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