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• W1545275724 abstract "Modern methods for numerical optimization calculate (or approximate) the matrix of second derivatives, the Hessian matrix, at each iteration. The recent arrival of robust software for automatic differentiation allows for the possibility of automatically computing the Hessian matrix, and the gradient, given a code to evaluate the objective function itself. However, for large-scale problems direct application of automatic differentiation may be unacceptably expensive. Recent work has shown that this cost can be dramatically reduced in the presence of sparsity. In this paper we show that for structured problems it is possible to apply automatic differentiation tools in an economical way even in the absence of sparsity in the Hessian. Keywords: Hessian matrix, automatic differentiation, structured computation, sparsity." @default.
• W1545275724 created "2016-06-24" @default.
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• W1545275724 creator A5085231322 @default.
• W1545275724 date "1998-01-01" @default.
• W1545275724 modified "2023-09-26" @default.
• W1545275724 title "Structure and Efficient Hessian Calculation" @default.
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• W1545275724 doi "https://doi.org/10.1007/978-1-4613-3335-7_3" @default.
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