Properties and practicability of convergence-guaranteed optimization methods derived from weak discrete gradients

DOI10.1007/s11075-024-01790-3MaRDI QIDQ6559452

Kansei Ushiyama, Shun Sato, Takayasu Matsuo

Publication date: 21 June 2024

Published in: Numerical Algorithms (Search for Journal in Brave)

zbMATH Keywords

convex optimizationnumerical analysisdiscrete gradientproximal gradient method

Mathematics Subject Classification ID

Numerical analysis (65-XX)

Cites Work

• Lectures on convex optimization
• Hamiltonian-conserving discrete canonical equations based on variational difference quotients
• First-order optimization algorithms via inertial systems with Hessian driven damping
• Time integration and discrete Hamiltonian systems
• Solving Ordinary Differential Equations I
• A new class of energy-preserving numerical integration methods
• Geometric Numerical Integration
• A Systematic Approach to Lyapunov Analyses of Continuous-Time Models in Convex Optimization
• Unnamed Item

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