The Complexity of Computing KKT Solutions of Quadratic Programs
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Abstract (arXiv)
It is well known that solving a (non-convex) quadratic program is NP-hard. We show that the problem remains hard even if we are only looking for a Karush-Kuhn-Tucker (KKT) point, instead of a global optimum. Namely, we prove that computing a KKT point of a quadratic polynomial over the domain [0,1]n is complete for the class CLS = PPADcapPLS.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Nov 2023.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2025The Complexity of Finding Local Optima in Contrastive Learningcited by 1
- 2026The Complexity of Min-Max Optimization for Quadratic Polynomialscited by 1
- 2024On the Hardness of Meaningful Local Guarantees in Nonsmooth Nonconvex Optimizationcited by 0
- 2026On Approximate Computation of Critical Pointscited by 0
- 2026The Complexity of Min-Max Optimization for Quadratic Polynomials15 Jun 2026
- 2026On Approximate Computation of Critical Points29 Jan 2026
- 2025The Complexity of Finding Local Optima in Contrastive Learning21 Sep 2025
- 2024On the Hardness of Meaningful Local Guarantees in Nonsmooth Nonconvex Optimization16 Sep 2024