TY - GEN

T1 - Strong NP-hardness for sparse optimization with concave penalty functions

AU - Chen, Yichen

AU - Ge, Dongdong

AU - Wang, Mengdi

AU - Wang, Zizhuo

AU - Ye, Yinyu

AU - Yin, Hao

N1 - Publisher Copyright:
© 2017 by the author(s).

PY - 2017

Y1 - 2017

N2 - Consider the regularized sparse minimization problem, which involves empirical sums of loss functions for n data points (each of dimension d) and a nonconvex sparsity penalty. We prove that finding an O(nC1dC2)-optimal solution to the regularized sparse optimization problem is strongly NP-hard for any C1, C2 ∈ [0, 1) such that C1 + C2 < 1. 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 problem in polynomial time, unless P = NP.

AB - Consider the regularized sparse minimization problem, which involves empirical sums of loss functions for n data points (each of dimension d) and a nonconvex sparsity penalty. We prove that finding an O(nC1dC2)-optimal solution to the regularized sparse optimization problem is strongly NP-hard for any C1, C2 ∈ [0, 1) such that C1 + C2 < 1. 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 problem in polynomial time, unless P = NP.

KW - Computational complexity

KW - Concave penalty

KW - NP-hardness

KW - Nonconvex optimization

KW - Sparsity

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UR - http://www.scopus.com/inward/citedby.url?scp=85048414541&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85048414541

T3 - 34th International Conference on Machine Learning, ICML 2017

SP - 1230

EP - 1251

BT - 34th International Conference on Machine Learning, ICML 2017

PB - International Machine Learning Society (IMLS)

T2 - 34th International Conference on Machine Learning, ICML 2017

Y2 - 6 August 2017 through 11 August 2017

ER -