A new framework for the computation of Hessians

DOI10.1080/10556788.2011.580098zbMath1311.65084arXiv2007.15040OpenAlexW2064980205MaRDI QIDQ2885473

Margarida Pinheiro Mello, Robert M. Gower

Publication date: 23 May 2012

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.15040

zbMATH Keywords

automatic differentiationsparse matricesHessian matrixreverse computation

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical optimization and variational techniques (65K10) Numerical differentiation (65D25)

Related Items (6)

A Hitchhiker's guide to automatic differentiation ⋮ Capitalizing on \textit{live} variables: new algorithms for efficient Hessian computation via automatic differentiation ⋮ On efficient Hessian computation using the edge pushing algorithm in Julia ⋮ Higher-order reverse automatic differentiation with emphasis on the third-order ⋮ Computing the sparsity pattern of Hessians using automatic differentiation ⋮ \texttt{Tenscalc}: a toolbox to generate fast code to solve nonlinear constrained minimizations and compute Nash equilibria

Uses Software

• ADOL-C
• CUTE
• CUTEr
• edge_push_sp

Cites Work

• Unnamed Item
• The polyadic structure of factorable function tensors with applications to high-order minimization techniques
• Test example for nonlinear programming codes
• An interior-point algorithm for nonconvex nonlinear programming
• On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
• Efficient Computation of Sparse Hessians Using Coloring and Automatic Differentiation
• Evaluating Derivatives
• Computing sparse Hessians with automatic differentiation
• Automatic Hessians by reverse accumulation
• Computational Graphs and Rounding Error
• CUTE
• Interior Methods for Nonlinear Optimization

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