On efficient Hessian computation using the edge pushing algorithm in Julia

DOI10.1080/10556788.2018.1480625zbMath1407.90310OpenAlexW2891583213WikidataQ121664066 ScholiaQ121664066MaRDI QIDQ4685595

Cosmin G. Petra, Joey Huchette, Miles Lubin, Fangwei Qiang

Publication date: 9 October 2018

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

Full work available at URL: https://www.osti.gov/biblio/1474340

zbMATH Keywords

Juliaautomatic differentiationedge pushingsparse Hessian computationalgebraic modelling languageJuMP

Mathematics Subject Classification ID

Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30) Linear programming (90C05) Numerical differentiation (65D25)

Related Items (1)

\texttt{Tenscalc}: a toolbox to generate fast code to solve nonlinear constrained minimizations and compute Nash equilibria

Uses Software

• AMPL
• LIBSVM
• ve08
• ColPack
• edge_push_sp
• Julia
• JuMP
• Livarh
• Livarhacc

Cites Work

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• Partitioned variable metric updates for large structured optimization problems
• Capitalizing on \textit{live} variables: new algorithms for efficient Hessian computation via automatic differentiation
• Local convergence analysis for partitioned quasi-Newton updates
• A new framework for the computation of Hessians
• Efficient Computation of Sparse Hessians Using Coloring and Automatic Differentiation
• ColPack
• New Acyclic and Star Coloring Algorithms with Application to Computing Hessians
• Evaluating Derivatives
• Trust Region Methods
• Computing the sparsity pattern of Hessians using automatic differentiation
• What Color Is Your Jacobian? Graph Coloring for Computing Derivatives
• JuMP: A Modeling Language for Mathematical Optimization

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