{"paper":{"title":"A Proof of the Exact Convergence Rate of Gradient Descent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Jungbin Kim","submitted_at":"2024-12-05T18:51:26Z","abstract_excerpt":"We prove the exact worst-case convergence rate of gradient descent for smooth strongly convex optimization on $\\mathbb{R}^d$. Concretely, assuming that the objective function $f$ is $\\mu$-strongly convex and $L$-smooth, we identify the smallest possible value of $\\tau$ for which the inequality $f(x_{N})-f_{*}\\leq\\tau\\|x_{0}-x_{*}\\|^{2}$ always holds. The result was previously conjectured by Drori and Teboulle for the case $\\mu=0$, and by Taylor, Hendrickx, and Glineur for the case $\\mu>0$."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04427","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.04427/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}