5 papers
Heavy-Ball Method under Randomized Schedules
Chang He, Shuzhong Zhang
We study how predefined randomized parameter schedules accelerate the heavy-ball method on general smooth convex objectives. Our analysis distinguishes two levels of randomization:…
New Results on the Polyak Stepsize: Tight Convergence Analysis and Universal Function Classes
Chang He, Wenzhi Gao, Bo Jiang +2
In this paper, we revisit a classical adaptive stepsize strategy for gradient descent: the Polyak stepsize (PolyakGD), originally proposed in Polyak (1969). We study the convergenc…
History-Aware Adaptive High-Order Tensor Regularization
Chang He, Bo Jiang, Yuntian Jiang +2
In this paper, we develop a new adaptive regularization method for minimizing a composite function, which is the sum of a th-order () Lipschitz continuous function and…
On Relatively Smooth Optimization over Riemannian Manifolds
Chang He, Jiaxiang Li, Bo Jiang +2
We study optimization over Riemannian embedded submanifolds, where the objective function is relatively smooth in the ambient Euclidean space. Such problems have broad applications…
Non-Stationary Bandit Convex Optimization: An Optimal Algorithm with Two-Point Feedback
Chang He, Bo Jiang, Shuzhong Zhang
This paper studies bandit convex optimization in non-stationary environments with two-point feedback, using dynamic regret as the performance measure. We propose an algorithm based…