Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Peak Estimation of Rational Systems using Convex Optimization (2311.08321v2)

Published 14 Nov 2023 in math.OC, cs.SY, and eess.SY

Abstract: This paper presents algorithms that upper-bound the peak value of a state function along trajectories of a continuous-time system with rational dynamics. The finite-dimensional but nonconvex peak estimation problem is cast as a convex infinite-dimensional linear program in occupation measures. This infinite-dimensional program is then truncated into finite-dimensions using the moment-Sum-of-Squares (SOS) hierarchy of semidefinite programs. Prior work on treating rational dynamics using the moment-SOS approach involves clearing dynamics to common denominators or adding lifting variables to handle reciprocal terms under new equality constraints. Our solution method uses a sum-of-rational method based on absolute continuity of measures. The Moment-SOS truncations of our program possess lower computational complexity and (empirically demonstrated) higher accuracy of upper bounds on example systems as compared to prior approaches.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (46)
  1. J. Němcová, M. Petreczky, and J. H. van Schuppen, “Towards a system theory of rational systems,” Operator Theory, Analysis and the State Space Approach: In Honor of Rien Kaashoek, pp. 327–359, 2018.
  2. John Wiley & Sons, 2005.
  3. R. Lewis and R. Vinter, “Relaxation of optimal control problems to equivalent convex programs,” Journal of Mathematical Analysis and Applications, vol. 74, no. 2, pp. 475–493, 1980.
  4. M. J. Cho and R. H. Stockbridge, “Linear Programming Formulation for Optimal Stopping Problems,” SIAM Journal on Control and Optimization, vol. 40, no. 6, pp. 1965–1982, 2002.
  5. P. Mohajerin Esfahani, T. Sutter, D. Kuhn, and J. Lygeros, “From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming,” SIAM Journal on Optimization, vol. 28, no. 3, pp. 1968–1998, 2018.
  6. J. B. Lasserre, “Global optimization with polynomials and the problem of moments,” SIAM Journal on Optimization, vol. 11, no. 3, pp. 796–817, 2001.
  7. D. Henrion, J. B. Lasserre, and C. Savorgnan, “Nonlinear optimal control synthesis via occupation measures,” in 2008 47th IEEE Conference on Decision and Control, pp. 4749–4754, IEEE, 2008.
  8. G. Fantuzzi and D. Goluskin, “Bounding Extreme Events in Nonlinear Dynamics Using Convex Optimization,” SIAM Journal on Applied Dynamical Systems, vol. 19, no. 3, pp. 1823–1864, 2020.
  9. J. Miller, D. Henrion, M. Sznaier, and M. Korda, “Peak Estimation for Uncertain and Switched Systems,” in 2021 60th IEEE Conference on Decision and Control (CDC), pp. 3222–3228, 2021.
  10. J. Miller and M. Sznaier, “Analysis and Control of Input-Affine Dynamical Systems using Infinite-Dimensional Robust Counterparts,” 2023. arXiv:2112.14838.
  11. J. Miller, M. Tacchi, M. Sznaier, and A. Jasour, “Peak Value-at-Risk Estimation for Stochastic Differential Equations using Occupation Measures,” 2023. arXiv:2303.16064.
  12. J. Miller, M. Korda, V. Magron, and M. Sznaier, “Peak Estimation of Time Delay Systems using Occupation Measures,” 2023. arXiv:2303.12863.
  13. J. Miller and M. Sznaier, “Peak Estimation of Hybrid Systems with Convex Optimization,” 2023. arXiv:2303.11490.
  14. D. Henrion and M. Korda, “Convex Computation of the Region of Attraction of Polynomial Control Systems,” IEEE Trans. Automat. Contr., vol. 59, p. 297–312, Feb 2014.
  15. A. Majumdar, R. Vasudevan, M. M. Tobenkin, and R. Tedrake, “Convex optimization of nonlinear feedback controllers via occupation measures,” The International Journal of Robotics Research, vol. 33, no. 9, pp. 1209–1230, 2014.
  16. M. Korda, D. Henrion, and C. N. Jones, “Inner approximations of the region of attraction for polynomial dynamical systems,” IFAC Proceedings Volumes, vol. 46, no. 23, pp. 534–539, 2013.
  17. N. Kariotoglou, S. Summers, T. Summers, M. Kamgarpour, and J. Lygeros, “Approximate dynamic programming for stochastic reachability,” in 2013 European Control Conference (ECC), pp. 584–589, IEEE, 2013.
  18. N. Schmid and J. Lygeros, “Probabilistic Reachability and Invariance Computation of Stochastic Systems using Linear Programming,” arXiv:2211.07544, 2022.
  19. A. Oustry, M. Tacchi, and D. Henrion, “Inner Approximations of the Maximal Positively Invariant Set for Polynomial Dynamical Systems,” IEEE Control Systems Letters, vol. 3, no. 3, pp. 733–738, 2019.
  20. M. Korda, D. Henrion, and C. N. Jones, “Convex Computation of the Maximum Controlled Invariant Set For Polynomial Control Systems,” SICON, vol. 52, no. 5, pp. 2944–2969, 2014.
  21. D. Goluskin, “Bounding extrema over global attractors using polynomial optimisation,” Nonlinearity, vol. 33, no. 9, p. 4878, 2020.
  22. C. Schlosser and M. Korda, “Converging outer approximations to global attractors using semidefinite programming,” Automatica, vol. 134, p. 109900, 2021.
  23. I. Tobasco, D. Goluskin, and C. R. Doering, “Optimal bounds and extremal trajectories for time averages in nonlinear dynamical systems,” Physics Letters A, vol. 382, no. 6, pp. 382–386, 2018.
  24. L. Ambrosio, L. Caffarelli, M. G. Crandall, L. C. Evans, N. Fusco, and L. Ambrosio, “Transport Equation and Cauchy Problem for Non-Smooth Vector Fields,” Calculus of Variations and Nonlinear Partial Differential Equations: With a historical overview by Elvira Mascolo, pp. 1–41, 2008.
  25. L. Ambrosio and G. Crippa, “Continuity equations and ODE flows with non-smooth velocity,” Proceedings of the Royal Society of Edinburgh Section A: Mathematics, vol. 144, no. 6, pp. 1191–1244, 2014.
  26. M. Souaiby, A. Tanwani, and D. Henrion, “Ensemble approximations for constrained dynamical systems using Liouville equation,” Automatica, vol. 149, p. 110836, 2023.
  27. F. Bugarin, D. Henrion, and J. B. Lasserre, “Minimizing the sum of many rational functions,” Mathematical Programming Computation, vol. 8, no. 1, pp. 83–111, 2016.
  28. J. P. Parker, D. Goluskin, and G. M. Vasil, “A study of the double pendulum using polynomial optimization,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 31, no. 10, 2021.
  29. V. Magron, M. Forets, and D. Henrion, “Semidefinite approximations of invariant measures for polynomial systems,” Discrete & Continuous Dynamical Systems - B, vol. 22, no. 11, p. 1–26, 2017.
  30. M. Newton and A. Papachristodoulou, “Rational neural network controllers,” arXiv:2307.06287, 2023.
  31. J. Miller and M. Sznaier, “Bounding the Distance to Unsafe Sets with Convex Optimization,” IEEE Transactions on Automatic Control, pp. 1–15, 2023. (Early Access).
  32. D. Hilbert, “Über die Darstellung definiter Formen als Summe von Formenquadraten,” Mathematische Annalen, vol. 32, no. 3, pp. 342–350, 1888.
  33. G. Blekherman, “There are Significantly More Nonnegative Polynomials than Sums of Squares,” Israel Journal of Mathematics, vol. 153, no. 1, pp. 355–380, 2006.
  34. P. A. Parrilo, Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization. PhD thesis, California Institute of Technology, 2000.
  35. M. Putinar, “Positive Polynomials on Compact Semi-algebraic Sets,” Indiana University Mathematics Journal, vol. 42, no. 3, pp. 969–984, 1993.
  36. J. Nie and M. Schweighofer, “On the complexity of Putinar’s Positivstellensatz,” Journal of Complexity, vol. 23, no. 1, pp. 135–150, 2007.
  37. M. Tacchi, “Convergence of Lasserre’s hierarchy: the general case,” Optimization Letters, vol. 16, no. 3, pp. 1015–1033, 2022.
  38. S. Gribling, S. Polak, and L. Slot, “A note on the computational complexity of the moment-SOS hierarchy for polynomial optimization,” arXiv:2305.14944, 2023.
  39. J. Wang, V. Magron, J. B. Lasserre, and N. H. A. Mai, “CS-TSSOS: Correlative and term sparsity for large-scale polynomial optimization,” ACM Transactions on Mathematical Software, vol. 48, no. 4, pp. 1–26, 2022.
  40. J. Wang, C. Schlosser, M. Korda, and V. Magron, “Exploiting Term Sparsity in Moment-SOS Hierarchy for Dynamical Systems,” IEEE Transactions on Automatic Control, 2023.
  41. M. Lubin, O. Dowson, J. D. Garcia, J. Huchette, B. Legat, and J. P. Vielma, “JuMP 1.0: Recent improvements to a modeling language for mathematical optimization,” Mathematical Programming Computation, 2023.
  42. F. Blanchini, D. Breda, G. Giordano, and D. Liessi, “Michaelis–Menten networks are structurally stable,” Automatica, vol. 147, p. 110683, 2023.
  43. C. Schlosser and M. Korda, “Sparse moment-sum-of-squares relaxations for nonlinear dynamical systems with guaranteed convergence,” arXiv preprint arXiv:2012.05572, 2020.
  44. M. Tacchi, Moment-SOS hierarchy for large scale set approximation. Application to power systems transient stability analysis. PhD thesis, Toulouse, INSA, 2021.
  45. A. Barvinok, A Course in Convexity. American Mathematical Society, 2002.
  46. P. J. Rabier and W. C. Rheinboldt, “Theoretical and Numerical Analysis of Differential-Algebraic Equations,” Handbook of Numerical Analysis, 2002.

Summary

We haven't generated a summary for this paper yet.