Adaptive Simulated Annealing (ASA) is a C-language code developed to statistically find the best global fit of a nonlinear constrained non-convex cost-function over a D-dimensional space. This algorithm permits an annealing schedule for ”temperature” T decreasing exponentially in annealing-time k, T = T_0 exp(-c k^1/D). The introduction of re-annealing also permits adaptation to changing sensitivities in the multi-dimensional parameter-space. This annealing schedule is faster than fast Cauchy annealing, where T = T_0/k, and much faster than Boltzmann annealing, where T = T_0/ln k. ASA has over 100 OPTIONS to provide robust tuning over many classes of nonlinear stochastic systems.

References in zbMATH (referenced in 77 articles , 1 standard article )

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  1. Alfeus, Mesias; Grasselli, Martino; Schlögl, Erik: A consistent stochastic model of the term structure of interest rates for multiple tenors (2020)
  2. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  3. Ferreiro, Ana M.; García-Rodríguez, José Antonio; Vázquez, Carlos; Costa e Silva, E.; Correia, A.: Parallel two-phase methods for global optimization on GPU (2019)
  4. Russo, Vincenzo; Torri, Gabriele: Calibration of one-factor and two-factor hull-white models using swaptions (2019)
  5. Taig, Efrat; Ben-Shahar, Ohad: Gradient surfing: a new deterministic approach for low-dimensional global optimization (2019)
  6. Geiping, Jonas; Moeller, Michael: Composite optimization by nonconvex majorization-minimization (2018)
  7. Kabanikhin, Sergey; Krivorotko, Olga; Kashtanova, Victoriya: A combined numerical algorithm for reconstructing the mathematical model for tuberculosis transmission with control programs (2018)
  8. Zhu, Song-Ping; He, Xin-Jiang: A modified Black-Scholes pricing formula for European options with bounded underlying prices (2018)
  9. Ermakov, S. M.; Kulikov, D. V.; Leora, S. N.: Towards the analysis of the simulated annealing method in the multiextremal case (2017)
  10. Kotov, Leonid N.; Severin, Pavel A.; Vlasov, Vladimir V.; Beznosikov, Dmitriy S.: Optimization of the magnetoelastic oscillations amplitudes in the ferrite plate (2017)
  11. Pál, László: Empirical study of the improved UNIRANDI local search method (2017)
  12. Valenzuela, Michael L.; Rozenblit, Jerzy W.: Learning using anti-training with sacrificial data (2016)
  13. Aguiar e O., Hime jun.; Petraglia, Antonio: Dimensional reduction in constrained global optimization on smooth manifolds (2015)
  14. Minford, Patrick; Ou, Zhirong; Wickens, Michael: Revisiting the Great Moderation : policy or luck? (2015)
  15. Dhabal, Supriya; Venkateswaran, Palaniandavar: Two-dimensional IIR filter design using simulated annealing based particle swarm optimization (2014)
  16. Kulczycki, Piotr; Łukasik, Szymon: An algorithm for reducing the dimension and size of a sample for data exploration procedures (2014)
  17. Le, Vo Phuong Mai; Matthews, Kent; Meenagh, David; Minford, Patrick; Xiao, Zhiguo: Banking and the macroeconomy in China: a banking crisis deferred? (2014)
  18. Silva, Ricardo M. A.; Resende, Mauricio G. C.; Pardalos, Panos M.: Finding multiple roots of a box-constrained system of nonlinear equations with a biased random-key genetic algorithm (2014)
  19. Turgut, Oguz Emrah; Turgut, Mert Sinan; Coban, Mustafa Turhan: Chaotic quantum behaved particle swarm optimization algorithm for solving nonlinear system of equations (2014)
  20. Le, Vo Phuong Mai; Meenagh, David; Minford, Patrick; Ou, Zhirong: What causes banking crises? An empirical investigation for the world economy (2013)

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