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Nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose a primal dual alternating proximal grad...
Nonconvex constrained optimization (NCO) has been one of the important research fields in optimization community. It has widely appeared in many application fields. However, challenges for solving NCO...
Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not indi...
We investigate the large-time behavior of three types of initial-boundary value problems for Hamilton-Jacobi Equations with nonconvex Hamiltonians.
Compressed sensing is a new scheme which shows the ability to recover sparse signal from fewer measurements, using l1 minimization. Recently, Chartrand and Staneva shown in [7] that the lp minimizatio...
We show, largely using convex examples, that most of the core results for limiting subdifferential calculus fail without additional restrictions in infinite dimensional Banach spaces.
In the scalar n-dimensional situation, the extreme points in the set of certain gradient Lp-Young measures are studied. For n = 1, such Young measures must be composed from Diracs, while for n µ...
Using some recent results from nonsmooth analysis, we prove the convergence of a new iterative scheme to a solution of a nonconvex equilibrium problem.
Iterative Schemes to Solve Nonconvex Variational Problems     Prox-regularity  Normal cone  Variational inequality       font style='font-size:12px;'> 2008/6/30
In this paper, we present several algorithms of the projection type to solve a class of nonconvex variational problems.
The self-scaling quasi-Newton method solves an unconstrained optimization problem by scaling the Hessian approximation matrix before it is updated at each iteration to avoid the possible large eigenva...

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