Abstract
We consider the problem of decomposing a multivariate polynomial as the difference of two convex polynomials. We introduce algebraic techniques which reduce this task to linear, second order cone, and semidefinite programming. This allows us to optimize over subsets of valid difference of convex decompositions (dcds) and find ones that speed up the convex–concave procedure. We prove, however, that optimizing over the entire set of dcds is NP-hard.
Original language | English (US) |
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Pages (from-to) | 69-94 |
Number of pages | 26 |
Journal | Mathematical Programming |
Volume | 169 |
Issue number | 1 |
DOIs | |
State | Published - May 1 2018 |
All Science Journal Classification (ASJC) codes
- Software
- General Mathematics
Keywords
- Algebraic decomposition of polynomials
- Conic relaxations
- Difference of convex programming
- Polynomial optimization