Friction-adaptive descent: a family of dynamics-based optimization methods
DOI10.3934/jcd.2023007arXiv2306.06738OpenAlexW4386080447MaRDI QIDQ6087370
Gabriel Stoltz, Aikaterini Karoni, Benedict J. Leimkuhler
Publication date: 15 November 2023
Published in: Journal of Computational Dynamics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2306.06738
Hamiltonian systemgradient descentNosé-Hoover dynamicscubic dampingconvex and nonconvex optimizationadaptive LangevinPolyak heavy ball method
Numerical optimization and variational techniques (65K10) Simulation of dynamical systems (37M05) Discretization methods and integrators (symplectic, variational, geometric, etc.) for dynamical systems (37M15) Computational molecular dynamics in statistical mechanics (82M37) Computational methods for attractors of dynamical systems (37M22)
Cites Work
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- Introductory lectures on convex optimization. A basic course.
- Exponential relaxation of the Nosé-Hoover thermostat under Brownian heating
- A gentle stochastic thermostat for molecular dynamics
- Second order conformal symplectic schemes for damped Hamiltonian systems
- Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise.
- A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
- Splitting methods
- Langevin Dynamics With General Kinetic Energies
- Rational Construction of Stochastic Numerical Methods for Molecular Sampling
- Hypocoercivity Properties of Adaptive Langevin Dynamics
- Some methods of speeding up the convergence of iteration methods
- A Projective Thermostatting Dynamics Technique
- Conformal Hamiltonian systems
- A Systematic Approach to Lyapunov Analyses of Continuous-Time Models in Convex Optimization
