4 papers
A Semismooth Newton Augmented Lagrangian Method for Sparse Spectral Risk Optimization
Rufeng Xiao, Rujun Jiang, Xudong Li +1
Empirical risk minimization is a standard and effective paradigm for learning predictive models by minimizing average loss. In high-stakes decision-making, however, an average-loss…
Solving Chance Constrained Programs via a Penalty based Difference of Convex Approach
Zhiping Li, Nan Jiang, Rujun Jiang
We develop two penalty based difference of convex (DC) algorithms for solving chance constrained programs. First, leveraging a rank-based DC decomposition of the chance constraint,…
An Alternating Direction Method of Multipliers for Utility-based Shortfall Risk Portfolio Optimization
Rufeng Xiao, Zhiping Li, Rujun Jiang
Utility-based shortfall risk (UBSR), a convex risk measure sensitive to tail losses, has gained popularity in recent years. However, research on computational methods for UBSR opti…
Riemannian Adaptive Regularized Newton Methods with Hölder Continuous Hessians
Chenyu Zhang, Rujun Jiang
This paper presents strong worst-case iteration and operation complexity guarantees for Riemannian adaptive regularized Newton methods, a unified framework encompassing both Rieman…