Abstract
In this paper, we provide the first provable linear-time (in terms of the number of nonzero entries of the input) algorithm for approximately solving the generalized trust region subproblem (GTRS) of minimizing a quadratic function over a quadratic constraint under some regularity condition. Our algorithm is motivated by and extends a recent linear-time algorithm for the trust region subproblem by Hazan and Koren [Math. Program., 158 (2016), pp. 363-381]. However, due to the nonconvexity and noncompactness of the feasible region, such an extension is nontrivial. Our main contribution is to demonstrate that under some regularity condition, the optimal solution is in a compact and convex set and lower and upper bounds of the optimal value can be computed in linear time. Using these properties, we develop a linear-time algorithm for the GTRS.
| Original language | English |
|---|---|
| Pages (from-to) | 915-932 |
| Journal | SIAM Journal on Optimization |
| Volume | 30 |
| Issue number | 1 |
| Online published | 12 Mar 2020 |
| DOIs | |
| Publication status | Published - 2020 |
Research Keywords
- approximation algorithms
- generalized trust region subproblem
- linear-time complexity
- semidefinite programming
Publisher's Copyright Statement
- COPYRIGHT TERMS OF DEPOSITED FINAL PUBLISHED VERSION FILE: © 2020 Society for Industrial and Applied Mathematics.
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Dive into the research topics of 'A LINEAR-TIME ALGORITHM FOR GENERALIZED TRUST REGION SUBPROBLEMS'. Together they form a unique fingerprint.Projects
- 2 Finished
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GRF: Exactness Conditions of SDP Relaxations for Generalized Extended Trust Region Subproblems
LI, D. (Principal Investigator / Project Coordinator)
1/01/18 → 16/11/20
Project: Research
-
GRF: Towards More Effective Convex Reformulation and Relaxation of Quadratically Constrained Quadratic Programming
LI, D. (Principal Investigator / Project Coordinator)
1/01/17 → 16/11/20
Project: Research
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