# Blog Notes

## Deriving the Fokker-Planck equation

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In the theory of dynamic systems, Fokker-Planck equation is used to describe the time evolution of the probability density function. It is a partial differential equation that describes how the density of a stochastic process changes as a function of time under the influence of a potential field. Some common application of it are in the study of Brownian motion, Ornstein–Uhlenbeck process, and in statistical physics. The motivation behind understanding the derivation is to study Levy flight processes that has caught my recent attention. Read more

## SGD without replacement

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This article is in continuation of my previous blog, and discusses about the work by Prateek Jain, Dheeraj Nagaraj and Praneeth Netrapalli 2019. The authors provide tight rates for SGD without replacement for general smooth, and general smooth and strongly convex functions using the method of exchangeable pairs to bound Wasserstein distances, and techniques from optimal transport. Read more

## Non-asymptotic rate for Random Shuffling for Quadratic functions

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This article is in continuation of my previous blog, and discusses about a section of the work by Jeffery Z. HaoChen and Suvrit Sra 2018, in which the authors come up with a non-asymptotic rate of $\mathcal{O}\left(\frac{1}{T^2} + \frac{n^3}{T^3} \right)$ for Random Shuffling Stochastic algorithm which is strictly better than that of SGD. Read more

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## Nesterov’s Acceleration

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This post contains a summary and survey of the Nesterov’s accelerated gradient descent method and some insightful implications that can be derived from it. We analyze the simple convex quadratic case and have a close look at the dynamics of the error vectors. Read more