Jan 4, 2015 · Abstract:Consider the regularized sparse minimization problem, which involves empirical sums of loss functions for n data points (each of ...

The result applies to a broad class of loss functions and sparse penalty functions. It suggests that one cannot even approximately solve the sparse optimization ...

Jun 19, 2017 · Abstract. Consider the regularized sparse minimization problem, which involves empirical sums of loss functions for n data points (each of ...

Dec 18, 2022 · Bibliographic details on Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions.

Dive into the research topics of 'Strong NP-hardness for sparse optimization with concave penalty functions'. Together they form a unique fingerprint. Sort by ...

It is shown that if the penalty function is concave but not linear in a neighborhood of zero, then the optimization problem is strongly NP-hard, ...

We show that if the penalty function is concave but not linear, then the optimization problem is strongly NP-hard. This result answers the complexity of many ...

In this section, we state the two critical assumptions that lead to the strong NP-hardness results: one for the penalty function p, the other one for the loss ...

Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions · Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning.

Abstract. In this paper, we consider three typical optimization problems with a convex loss function and a nonconvex sparse penalty or constraint. For the ...