The Complexity of Computing KKT Solutions of Quadratic Programs
John Fearnley, Paul W. Goldberg, Alexandros Hollender, Rahul Savani
2024Year
1Citations
4Top-tier citations
Abstract
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 = PPAD ∩ PLS.
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Cited by top-tier papers4
- PPAD-Membership for Problems with Exact Rational Solutions: A General Approach via Convex OptimizationAris Filos-Ratsikas, Kristoffer Arnsfelt Hansen, Kasper Høgh, Alexandros HollenderSTOC 2024 · 4 citations
- The Complexity of Finding Local Optima in Contrastive LearningJingming Yan, Yiyuan Luo, Vaggos Chatziafratis, Ioannis Panageas et al.NeurIPS 2025 · 2 citations
- The Computational Complexity of Finding Second-Order Stationary PointsAndreas Kontogiannis, Vasilis Pollatos, Sotiris Kanellopoulos, Panayotis Mertikopoulos et al.ICML 2024 · 1 citation
- The Complexity of Two-Team Polymatrix Games with Independent AdversariesAlexandros Hollender, Gilbert Maystre, Sai Ganesh NagarajanICLR 2025
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- SNARGs for bounded depth computations and PPAD hardness from sub-exponential LWERuta Jawale, Yael Tauman Kalai, Dakshita Khurana, Rachel Yun ZhangSTOC 2021 · 61 citations
- The complexity of gradient descent: CLS = PPAD ∩ PLSJohn Fearnley, Paul W. Goldberg, Alexandros Hollender, Rahul SavaniSTOC 2021 · 23 citations
- Settling the complexity of Nash equilibrium in congestion gamesYakov Babichenko, Aviad RubinsteinSTOC 2021 · 4 citations
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