Some Applications of Polynomial Optimization in Operations Research and Real-Time Decision Making
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Abstract (arXiv)
We demonstrate applications of algebraic techniques that optimize and certify polynomial inequalities to problems of interest in the operations research and transportation engineering communities. Three problems are considered: (i) wireless coverage of targeted geographical regions with guaranteed signal quality and minimum transmission power, (ii) computing real-time certificates of collision avoidance for a simple model of an unmanned vehicle (UV) navigating through a cluttered environment, and (iii) designing a nonlinear hovering controller for a quadrotor UV, which has recently been used for load transportation. On our smaller-scale applications, we apply the sum of squares (SOS) relaxation and solve the underlying problems with semidefinite programming. On the larger-scale or real-time applications, we use our recently introduced "SDSOS Optimization" techniques which result in second order cone programs. To the best of our knowledge, this is the first study of real-time applications of sum of squares techniques in optimization and control. No knowledge in dynamics and control is assumed from the reader.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2015.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 4; 2025: 2; 2026: 1.
11 papers.
- 2023AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learningcited by 16
- 2022Learning for Spatial Branching: An Algorithm Selection Approachcited by 6
- 2024Actor-Critic Physics-informed Neural Lyapunov Controlcited by 6
- 2025Learning Conservative Neural Control Barrier Functions from Offline Datacited by 4
- 2022Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safetycited by 2
- 2023Learning Polynomial Problems with SL(2,ℝ) Equivariancecited by 2
- 2024Neural Control and Certificate Repair via Runtime Monitoringcited by 2
- 2024Certified Policy Verification and Synthesis for MDPs under Distributional Reach-avoidance Propertiescited by 1
- 2024Combining Neural Networks and Symbolic Regression for Analytical Lyapunov Function Discoverycited by 1
- 2025Analytical Lyapunov Function Discovery: An RL-based Generative Approachcited by 1
- 2026Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networkscited by 0
- 2026Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networks16 Mar 2026
- 2025Learning Conservative Neural Control Barrier Functions from Offline Data1 May 2025
- 2025Analytical Lyapunov Function Discovery: An RL-based Generative Approach4 Feb 2025
- 2024Neural Control and Certificate Repair via Runtime Monitoring17 Dec 2024
- 2024Combining Neural Networks and Symbolic Regression for Analytical Lyapunov Function Discovery21 Jun 2024
- 2024Certified Policy Verification and Synthesis for MDPs under Distributional Reach-avoidance Properties7 May 2024
- 2024Actor-Critic Physics-informed Neural Lyapunov Control13 Mar 2024
- 2023Learning Polynomial Problems with SL(2,ℝ) Equivariance4 Dec 2023
- 2023AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning24 Jan 2023
- 2022Learning for Spatial Branching: An Algorithm Selection Approach22 Apr 2022
- 2022Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety29 Mar 2022