A gradient-based continuous method for large-scale optimization problems

DOI10.1007/s10898-004-5700-1zbMath1090.90147OpenAlexW2080637608MaRDI QIDQ1781969

Liqun Qi, Li-Zhi Liao, Hon-Wah Tam

Publication date: 9 June 2005

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-004-5700-1

zbMATH Keywords

Large-scale optimizationOrdinary differential equationContinuous method

Mathematics Subject Classification ID

Large-scale problems in mathematical programming (90C06) Interior-point methods (90C51) Nonlinear ordinary differential equations and systems (34A34)

Related Items (6)

Convergence analysis of a global optimization algorithm using stochastic differential equations ⋮ On the global convergence rate of the gradient descent method for functions with Hölder continuous gradients ⋮ Linearly constrained global optimization and stochastic differential equations ⋮ Identification of flexural rigidity in a Kirchhoff plates model using a convex objective and continuous Newton method ⋮ A Kronecker approximation with a convex constrained optimization method for blind image restoration ⋮ A new trust region method with adaptive radius

Cites Work

• Automatic Selection of Methods for Solving Stiff and Nonstiff Systems of Ordinary Differential Equations
• Testing Unconstrained Optimization Software
• Neural networks and physical systems with emergent collective computational abilities.
• Stability analysis of gradient-based neural networks for optimization problems

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