Limited memory BFGS method based on a high-order tensor model

DOI10.1007/S10589-014-9678-4zbMath1309.90050OpenAlexW2147564936MaRDI QIDQ2340525

Fahimeh Biglari, Ali Ebadian

Publication date: 20 April 2015

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-014-9678-4

zbMATH Keywords

curvature approximationR-linear convergencemodified quasi-Newton equationlarge scale nonlinear optimizationmodified limited memory quasi-Newton method

Mathematics Subject Classification ID

Large-scale problems in mathematical programming (90C06) Methods of quasi-Newton type (90C53)

Related Items (4)

Scaling damped limited-memory updates for unconstrained optimization ⋮ A hybrid quasi-Newton method with application in sparse recovery ⋮ Two-phase quasi-Newton method for unconstrained optimization problem ⋮ Diagonally scaled memoryless quasi-Newton methods with application to compressed sensing

Uses Software

• L-BFGS

Cites Work

• On the limited memory BFGS method for large scale optimization
• Optimization theory and methods. Nonlinear programming
• Properties and numerical performance of quasi-Newton methods with modified quasi-Newton equations
• Benchmarking optimization software with performance profiles.
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