Comparing different subgradient methods for solving convex optimization problems with functional constraints

Code

• github.com/dinhthilan/cop
  OfficialIn papernone★ 2

Abstract

We consider the problem of minimizing a convex, nonsmooth function subject to a closed convex constraint domain. The methods that we propose are reforms of subgradient methods based on Metel--Takeda's paper [Optimization Letters 15.4 (2021): 1491-1504] and Boyd's works [Lecture notes of EE364b, Stanford University, Spring 2013-14, pp. 1-39]. While the former has complexity O(^-2r) for all r> 1, the complexity of the latter is O(^-2). We perform some comparisons between these two methods using several test examples.

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