MultiComposite Nonconvex Optimization for Training Deep Neural Networks
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Publication:5114402
DOI10.1137/18M1231559zbMath1445.90086OpenAlexW3036100489MaRDI QIDQ5114402
Ying Cui, Ziyu He, Jong-Shi Pang
Publication date: 22 June 2020
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/18m1231559
nonconvexityexact penalizationmajorization-minimizationsemismooth Newton methodnondifferentiablitydeep neural network
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Cites Work
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- Partial penalization for the solution of generalized Nash equilibrium problems
- Convex analysis approach to d. c. programming: Theory, algorithms and applications
- Multilayer feedforward networks are universal approximators
- A globally convergent Newton method for convex \(SC^ 1\) minimization problems
- Deterministic global optimization. Theory, methods and applications
- Stochastic subgradient method converges on tame functions
- Introduction to Piecewise Differentiable Equations
- Computing B-Stationary Points of Nonsmooth DC Programs
- Exact penalization via dini and hadamard conditional derivatives
- Composite Difference-Max Programs for Modern Statistical Estimation Problems
- Stochastic Model-Based Minimization of Weakly Convex Functions
- Optimization Methods for Large-Scale Machine Learning
- Finite-Dimensional Variational Inequalities and Complementarity Problems
- ADMM for multiaffine constrained optimization
- Computing the Best Approximation over the Intersection of a Polyhedral Set and the Doubly Nonnegative Cone
- Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming
- A Fast Learning Algorithm for Deep Belief Nets
- A Stochastic Approximation Method
