Group-Sparse Model Selection: Hardness and Relaxations
نویسندگان
چکیده
منابع مشابه
Sparse weighted voting classifier selection and its linear programming relaxations
Article history: Received 14 January 2011 Received in revised form 8 March 2012 Accepted 8 March 2012 Available online 9 March 2012 Communicated by W.-L. Hsu
متن کاملSparse learning via Boolean relaxations
We introduce novel relaxations for cardinality-constrained learning problems, including least-squares regression as a special but important case. Our approach is based on reformulating a cardinality-constrained problem exactly as a Boolean program, to which standard convex relaxations such as the Lasserre and Sherali-Adams hierarchies can be applied. We analyze the first-order relaxation in det...
متن کاملHardness Results and Algebraic Relaxations for Control of Underactuated Robots
In this paper, we study the computational complexity of several important decision problems that arise in robotic control applications and provide algebraic relaxations for designing controllers for such tasks. First, we show that the following decision problems are strongly NP-hard: deciding local asymptotic stability of trigonometric vector fields of degree four, invariance of a ball for poly...
متن کاملNP-Hardness and Inapproximability of Sparse PCA
We give a reduction from clique to establish that sparse PCA is NP-hard. The reduction has a gap which we use to exclude an FPTAS for sparse PCA (unless P=NP). Under weaker complexity assumptions, we also exclude polynomial constant-factor approximation algorithms.
متن کاملHardness study and selection algorithms
Horizontal Partitioning has been largely adopted by the database community, where it took a significant part in the physical design process. Actually, it is supported by most commercial database systems (DBMS), where a native Data Definition Language for decomposing tables/materialized views using various modes is proposed. In traditional databases, horizontal partitioning has been largely stud...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Information Theory
سال: 2016
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2016.2602222