Online Learning and Optimization from Continuous to Discrete Time
1:05:28
Microsoft
Online Learning and Optimization from Continuous to Discrete Time
Many discrete algorithms for convex optimization and online learning can be interpreted as a discretization of a continuous-time process. Perhaps the simplest and oldest example is gradient descent, which is the discretization of the ODE $dot X = -nabla f(X)$. Studying the continuous-time process offers many advantages: the analysis is often ...
19 Feb 2016
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