Gradient Descent Converges to Minimizers
arXiv:1602.04915
Abstract
We show that gradient descent converges to a local minimizer, almost surely with random initialization. This is proved by applying the Stable Manifold Theorem from dynamical systems theory.
Submitted to COLT 2016
References in corpus (5)
- The Loss Surfaces of Multilayer Networks
- Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
- Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picture
- Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Method
- On the saddle point problem for non-convex optimization
Cited by in corpus (68)
- Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management
- On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
- Matrix Completion has No Spurious Local Minimum
- Asynchronous Stochastic Gradient Descent with Delay Compensation
- Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
- No bad local minima: Data independent training error guarantees for multilayer neural networks
- The Mechanics of n-Player Differentiable Games
- Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks
- Global Optimality in Low-rank Matrix Optimization
- Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
- Escaping Saddles with Stochastic Gradients
- Learning explanations that are hard to vary
- Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization
- Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks
- A near-stationary subspace for ridge approximation
- Competitive Gradient Descent
- Gradient Descent Only Converges to Minimizers: Non-Isolated Critical Points and Invariant Regions
- Beyond Convexity -- Contraction and Global Convergence of Gradient Descent
- Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
- Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
- Implicit Geometric Regularization for Learning Shapes
- The Global Optimization Geometry of Low-Rank Matrix Optimization
- Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations
- On the Benefit of Width for Neural Networks: Disappearance of Bad Basins
- Local Saddle Point Optimization: A Curvature Exploitation Approach
- Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solutions for Nonconvex Distributed Optimization
- Fast Rates for Empirical Risk Minimization of Strict Saddle Problems
- Deep Hamiltonian networks based on symplectic integrators
- Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
- Convergence to Second-Order Stationarity for Constrained Non-Convex Optimization
- Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness
- A general system of differential equations to model first order adaptive algorithms
- Fast Global Convergence for Low-rank Matrix Recovery via Riemannian Gradient Descent with Random Initialization
- Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization
- Revisiting Landscape Analysis in Deep Neural Networks: Eliminating Decreasing Paths to Infinity
- When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima?
- Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
- Stochastic Heavy Ball
- Nonconvex Robust Low-rank Matrix Recovery
- A Survey on Fault-tolerance in Distributed Optimization and Machine Learning
- A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent
- Deep Online Convex Optimization with Gated Games
- Communication-Efficient Federated Learning with Compensated Overlap-FedAvg
- Implicit Regularization and Convergence for Weight Normalization
- Learning from Sparse Demonstrations
- Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature
- Exit Time Analysis for Approximations of Gradient Descent Trajectories Around Saddle Points
- Fast Convergence for Langevin Diffusion with Manifold Structure
- Analysis of Asymptotic Escape of Strict Saddle Sets in Manifold Optimization
- Block Coordinate Descent Only Converge to Minimizers
- On The Geometric Analysis of A Quartic-quadratic Optimization Problem under A Spherical Constraint
- Global Optimality in Distributed Low-rank Matrix Factorization
- Lecture notes on non-convex algorithms for low-rank matrix recovery
- Aggressive Local Search for Constrained Optimal Control Problems with Many Local Minima
- An Essay on Optimization Mystery of Deep Learning
- On Alignment in Deep Linear Neural Networks
- Distributed stochastic optimization with large delays
- A Variant of Gradient Descent Algorithm Based on Gradient Averaging
- Asymptotic Escape of Spurious Critical Points on the Low-rank Matrix Manifold
- FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints
- One-dimensional System Arising in Stochastic Gradient Descent
- The Landscape of Multi-Layer Linear Neural Network From the Perspective of Algebraic Geometry
- Training Linear Neural Networks: Non-Local Convergence and Complexity Results
- How regularization affects the critical points in linear networks
- Depth Descent Synchronization in
- Generalization Error Bounds for Optimization Algorithms via Stability
- Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience