<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Convergence on Yuchen Du</title><link>https://dudusoar.github.io/tags/convergence/</link><description>Recent content in Convergence on Yuchen Du</description><generator>Hugo -- 0.164.0</generator><language>en-us</language><lastBuildDate>Fri, 17 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://dudusoar.github.io/tags/convergence/index.xml" rel="self" type="application/rss+xml"/><item><title>Where Stochastic Approximation Begins: Learning from a Noisy Target</title><link>https://dudusoar.github.io/posts/stochastic-approximation/01-learning-from-a-noisy-target/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://dudusoar.github.io/posts/stochastic-approximation/01-learning-from-a-noisy-target/</guid><description>Stochastic approximation begins when the true target is hidden and each update can use only a noisy estimate of it.</description></item><item><title>Model Error as Distance to the True Target</title><link>https://dudusoar.github.io/posts/stochastic-approximation/02-model-error-as-distance-to-target/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://dudusoar.github.io/posts/stochastic-approximation/02-model-error-as-distance-to-target/</guid><description>Why convergence should track distance to the true target rather than the size of the latest update.</description></item><item><title>What a Noisy Iteration Needs in Order to Converge</title><link>https://dudusoar.github.io/posts/stochastic-approximation/03-what-convergence-requires/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://dudusoar.github.io/posts/stochastic-approximation/03-what-convergence-requires/</guid><description>A compact map of the step-size and noise assumptions behind stochastic-approximation convergence.</description></item><item><title>The Robbins–Monro Conditions Balance Learning and Noise</title><link>https://dudusoar.github.io/posts/stochastic-approximation/12-robbins-monro-conditions/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://dudusoar.github.io/posts/stochastic-approximation/12-robbins-monro-conditions/</guid><description>The step sizes must remain large enough to keep correcting error and become small enough to suppress accumulated noise.</description></item><item><title>Why the Step Sizes Must Sum to Infinity</title><link>https://dudusoar.github.io/posts/stochastic-approximation/13-why-step-sizes-must-sum-to-infinity/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://dudusoar.github.io/posts/stochastic-approximation/13-why-step-sizes-must-sum-to-infinity/</guid><description>A finite total step size can make an iteration stop moving before its error has disappeared.</description></item><item><title>Expand the Error into Initial Bias and Historical Noise</title><link>https://dudusoar.github.io/posts/stochastic-approximation/18-expanding-the-error-recursion/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://dudusoar.github.io/posts/stochastic-approximation/18-expanding-the-error-recursion/</guid><description>The explicit error formula separates the residual initial condition from the weighted sum of all past disturbances.</description></item><item><title>How Historical Noise Is Attenuated</title><link>https://dudusoar.github.io/posts/stochastic-approximation/20-how-noise-variance-decays/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://dudusoar.github.io/posts/stochastic-approximation/20-how-noise-variance-decays/</guid><description>Each disturbance is controlled twice: by its entry coefficient and by every contraction that follows it.</description></item><item><title>Two Proof Routes Explain Different Levels of Convergence</title><link>https://dudusoar.github.io/posts/stochastic-approximation/22-two-proof-routes-to-dvoretzky-convergence/</link><pubDate>Fri, 17 Jul 2026 00:00:00 +0000</pubDate><guid>https://dudusoar.github.io/posts/stochastic-approximation/22-two-proof-routes-to-dvoretzky-convergence/</guid><description>Expectation and variance explain distributional concentration; the energy and martingale route proves almost-sure convergence.</description></item></channel></rss>