1 citations · 1 across the 10 of their papers we have counts for
11 papers
Optimal Recursive Composition and Dyadic Phase Laws for Gradient Descent with Predetermined Stepsizes
Yu Liu, Kang Chen, Rujun Jiang +1
Predetermined stepsize schedules featuring carefully chosen long steps have recently been shown to accelerate gradient descent (GD) on smooth convex functions. A prominent class of…
Differentiable Partitioning With Dual-Max 3D Wirelength Model and Hybrid Bonding Terminal Awareness for Optimized 3D Placement
Liwen Jiang, Xu Shi, Rufeng Xiao +4
Research on 3D integrated circuits (3D-ICs) physical design has expanded rapidly in recent years, driven by the increasing adoption of hybrid bonding and the demand for higher inte…
Adaptive Algorithms for Nonconvex Bilevel Optimization under PŁ Conditions
Xu Shi, Yinglin Du, Rufeng Xiao +1
Existing methods for nonconvex bilevel optimization (NBO) require prior knowledge of first- and second-order problem-specific parameters (e.g., Lipschitz constants and the Polyak-Ł…
An Adaptive Algorithm for Bilevel Optimization on Riemannian Manifolds
Xu Shi, Rufeng Xiao, Rujun Jiang
Existing methods for solving Riemannian bilevel optimization (RBO) problems require prior knowledge of the problem's first- and second-order information and curvature parameter of…
Accelerated Gradient Descent by Concatenation of Stepsize Schedules
Zehao Zhang, Rujun Jiang
This work considers stepsize schedules for gradient descent on smooth convex objectives. We extend the existing literature and propose a unified technique for constructing stepsize…
Value-Function Root-Finding Algorithms for Composite Convex Simple Bilevel Optimization
Rujun Jiang, Xu Shi, Weizheng Song +1
This paper considers composite convex simple bilevel optimization (SBO), which minimizes a composite convex function over the solution set of another composite convex minimization…
SOREL: A Stochastic Algorithm for Spectral Risks Minimization
Yuze Ge, Rujun Jiang
The spectral risk has wide applications in machine learning, especially in real-world decision-making, where people are not only concerned with models' average performance. By assi…
Near-Optimal Convex Simple Bilevel Optimization with a Bisection Method
Jiulin Wang, Xu Shi, Rujun Jiang
This paper studies a class of simple bilevel optimization problems where we minimize a composite convex function at the upper-level subject to a composite convex lower-level proble…
Lower-level Duality Based Reformulation and Majorization Minimization Algorithm for Hyperparameter Optimization
He Chen, Haochen Xu, Rujun Jiang +1
Hyperparameter tuning is an important task of machine learning, which can be formulated as a bilevel program (BLP). However, most existing algorithms are not applicable for BLP wit…
Penalty-based Methods for Simple Bilevel Optimization under Hölderian Error Bounds
Pengyu Chen, Xu Shi, Rujun Jiang +1
This paper investigates simple bilevel optimization problems where we minimize an upper-level objective over the optimal solution set of a convex lower-level objective. Existing me…
Hölderian error bounds and Kurdyka-Łojasiewicz inequality for the trust region subproblem
Rujun Jiang, Xudong Li
In this paper, we study the local variational geometry of the optimal solution set of the trust region subproblem (TRS), which minimizes a general, possibly nonconvex, quadratic fu…