Publication | Open Access
Gradient Descent Converges to Minimizers
123
Citations
18
References
2016
Year
EngineeringMachine LearningRandom InitializationStochastic OptimizationStable Manifold TheoremVariational AnalysisConvex OptimizationStochastic Dynamical SystemGradient Descent ConvergesGlobal AnalysisComputer ScienceProbability TheoryInfinite Dimensional ProblemNondifferentiable OptimizationApproximation TheoryConvergence Analysis
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.
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