DC Decomposition of Nonconvex Polynomials with Algebraic Techniques
Not on the map. Why?
Abstract (arXiv)
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 (CCP). We prove, however, that optimizing over the entire set of dcds is NP-hard.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Oct 2015.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 0; 2025: 0; 2026: 3.
6 papers.
- 2023Higher-Order Newton Methods with Polynomial Work per Iterationcited by 3
- 2021Sums of Separable and Quadratic Polynomialscited by 2
- 2021Coordinate Descent Methods for DC Minimization: Optimality Conditions and Global Convergencecited by 2
- 2026On Approximate Computation of Critical Pointscited by 0
- 2026RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programscited by 0
- 2026Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimizationcited by 0
- 2026Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization26 Jun 2026
- 2026RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs22 May 2026
- 2026On Approximate Computation of Critical Points29 Jan 2026
- 2023Higher-Order Newton Methods with Polynomial Work per Iteration10 Nov 2023
- 2021Coordinate Descent Methods for DC Minimization: Optimality Conditions and Global Convergence9 Sep 2021
- 2021Sums of Separable and Quadratic Polynomials11 May 2021