scientific article; zbMATH DE number 7370630
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Publication:4999082
Unnamed Author, Michael I. Jordan, Ashia C. Wilson
Publication date: 9 July 2021
Full work available at URL: https://jmlr.csail.mit.edu/papers/v22/20-195.html
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Cites Work
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- Smooth minimization of non-smooth functions
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- Optimized first-order methods for smooth convex minimization
- Gradient methods for minimizing composite functions
- Accelerating the cubic regularization of Newton's method on convex problems
- A second-order gradient-like dissipative dynamical system with Hessian-driven damping. Application to optimization and mechanics.
- Introductory lectures on convex optimization. A basic course.
- Numerical methods for ordinary differential equations in the 20th century
- Performance of first-order methods for smooth convex minimization: a novel approach
- Quasi-monotone subgradient methods for nonsmooth convex minimization
- Adaptive restart for accelerated gradient schemes
- A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
- Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints
- A variational perspective on accelerated methods in optimization
- Hessian Riemannian Gradient Flows in Convex Programming
- Viscosity Solutions of Minimization Problems
- Learning representations by back-propagating errors
- Some methods of speeding up the convergence of iteration methods
