6 citations · 10 across the 13 of their papers we have counts for
14 papers
Computing Kurdyka-Łojasiewicz exponents via composition and symmetry
Cédric Josz, Wenqing Ouyang
We devise calculus rules for the Kurdyka-Łojasiewicz exponent using the rank theorem and Lie group actions. They apply to a wide class of composite and invariant functions, and are…
A linesearch-type normal map-based semismooth Newton method for nonsmooth nonconvex composite optimization
Hanfeng Zeng, Wenqing Ouyang, Andre Milzarek
We propose a novel linesearch variant of the trust region normal map-based semismooth Newton method developed in [Ouyang and Milzarek, Math. Program. 212(1-2), 389--435 (2025)] for…
A MINRES-based Linesearch Algorithm for Nonconvex Optimization with Non-positive Curvature Detection
Hanfeng Zeng, Yang Liu, Wenqing Ouyang +1
We propose a MINRES-based Newton-type algorithm for solving unconstrained nonconvex optimization problems. Our approach uses the minimal residual method (MINRES), a well-known solv…
Reachability of gradient descent
Cédric Josz, Wenqing Ouyang
We show that gradient descent can converge to any local minimum of a smooth semi-algebraic function. This holds if the step sizes are nonsummable and sufficiently small. The same r…
Kurdyka-Łojasiewicz exponent via square transformation
Wenqing Ouyang
We consider one of the most common reparameterization techniques, the square transformation. Assuming the original objective function is the sum of a smooth function and a polyhedr…
Burer-Monteiro factorizability of nuclear norm regularized optimization
Wenqing Ouyang, Ting Kei Pong, Man-Chung Yue
This paper studies the relationship between the nuclear norm-regularized minimization problem, which minimizes the sum of a function and a positive multiple of the nuclea…
Kurdyka-Łojasiewicz exponent via Hadamard parametrization
Wenqing Ouyang, Yuncheng Liu, Ting Kei Pong +1
We consider a class of -regularized optimization problems and the associated smooth "over-parameterized" optimization problems built upon the Hadamard parametrization, or e…
Variational Properties of Decomposable Functions. Part II: Strong Second-Order Theory
Wenqing Ouyang, Andre Milzarek
Local superlinear convergence of the semismooth Newton method usually necessitates assumptions on the uniform invertibility of the utilized, generalized Jacobian matrices, such as,…
Variational Properties of Decomposable Functions. Part I: Strict Epi-Calculus and Applications
Wenqing Ouyang, Andre Milzarek
This work provides a systematic study of the variational properties of decomposable functions which are compositions of an outer support function and an inner smooth mapping under…
Descent Properties of an Anderson Accelerated Gradient Method With Restarting
Wenqing Ouyang, Yang Liu, Andre Milzarek
Anderson Acceleration (AA) is a popular acceleration technique to enhance the convergence of fixed-point iterations. The analysis of AA approaches typically focuses on the converge…
A trust region-type normal map-based semismooth Newton method for nonsmooth nonconvex composite optimization
Wenqing Ouyang, Andre Milzarek
We propose a novel trust region method for solving a class of nonsmooth, nonconvex composite-type optimization problems. The approach embeds inexact semismooth Newton steps for fin…
Anderson Acceleration for Nonconvex ADMM Based on Douglas-Rachford Splitting
Wenqing Ouyang, Yue Peng, Yuxin Yao +2
The alternating direction multiplier method (ADMM) is widely used in computer graphics for solving optimization problems that can be nonsmooth and nonconvex. It converges quickly t…
Nonmonotone Globalization for Anderson Acceleration via Adaptive Regularization
Wenqing Ouyang, Jiong Tao, Andre Milzarek +1
Anderson acceleration (AA) is a popular method for accelerating fixed-point iterations, but may suffer from instability and stagnation. We propose a globalization method for AA to…
Accelerating ADMM for Efficient Simulation and Optimization
Juyong Zhang, Yue Peng, Wenqing Ouyang +1
The alternating direction method of multipliers (ADMM) is a popular approach for solving optimization problems that are potentially non-smooth and with hard constraints. It has bee…