Algorithm differentiation of implicit functions and optimal values In applied optimization, an understanding of the sensitivity of the optimal value to changes in structural parameters is often essential. Applications include parametric optimization, saddle point problems, Benders decompositions, and multilevel optimization. In this paper we adapt a known automatic differentiation (AD) technique for obtaining derivatives of implicitly defined functions for application to optimal value functions. The formulation we develop is well suited to the evaluation of first and second derivatives of optimal values. The result is a method that yields large savings in time and memory. The savings are demonstrated by a Benders decomposition example using both the ADOL-C and CppAD packages. Some of the source code for these comparisons is included to aid testing with other hardware and compilers, other AD packages, as well as future versions of ADOL-C and CppAD. The source code also serves as an aid in the implementation of the method for actual applications. In addition, it demonstrates how multiple C++ operator overloading AD packages can be used with the same source code. This provides motivation for the coding numerical routines where the floating point type is a C++ template parameter.

References in zbMATH (referenced in 38 articles )

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  1. Lundell, Andreas; Kronqvist, Jan; Westerlund, Tapio: The supporting hyperplane optimization toolkit for convex MINLP (2022)
  2. Müller, Benjamin; Muñoz, Gonzalo; Gasse, Maxime; Gleixner, Ambros; Lodi, Andrea; Serrano, Felipe: On generalized surrogate duality in mixed-integer nonlinear programming (2022)
  3. James Yang: FastAD: Expression Template-Based C++ Library for Fast and Memory-Efficient Automatic Differentiation (2021) arXiv
  4. Michaud, N., de Valpine, P., Turek, D., Paciorek, C. J., Nguyen, D.: Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages (2021) not zbMATH
  5. Anita K. Nandi, Tim C. D. Lucas, Rohan Arambepola, Peter Gething, Daniel J. Weiss: disaggregation: An R Package for Bayesian Spatial Disaggregation Modelling (2020) arXiv
  6. Gleixner, Ambros; Maher, Stephen J.; Müller, Benjamin; Pedroso, João Pedro: Price-and-verify: a new algorithm for recursive circle packing using Dantzig-Wolfe decomposition (2020)
  7. Listov, Petr; Jones, Colin: PolyMPC: an efficient and extensible tool for real-time nonlinear model predictive tracking and path following for fast mechatronic systems (2020)
  8. Müller, Benjamin; Serrano, Felipe; Gleixner, Ambros: Using two-dimensional projections for stronger separation and propagation of bilinear terms (2020)
  9. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  10. Askham, Travis; Kutz, J. Nathan: Variable projection methods for an optimized dynamic mode decomposition (2018)
  11. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  12. Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
  13. Kulshreshtha, K.; Narayanan, S. H. K.; Bessac, J.; MacIntyre, K.: Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C (2018)
  14. van Leeuwen, Tristan; Maretzke, Simon; Batenburg, K. Joost: Automatic alignment for three-dimensional tomographic reconstruction (2018)
  15. Cocher, Emmanuel; Chauris, Hervé; Plessix, René-Édouard: Seismic iterative migration velocity analysis: two strategies to update the velocity model (2017)
  16. Gleixner, Ambros M.; Berthold, Timo; Müller, Benjamin; Weltge, Stefan: Three enhancements for optimization-based bound tightening (2017)
  17. Kasper Kristensen and Anders Nielsen and Casper Berg and Hans Skaug and Bradley Bell: TMB: Automatic Differentiation and Laplace Approximation (2016) not zbMATH
  18. Sluşanschi, Emil I.; Dumitrel, Vlad: ADiJaC -- automatic differentiation of Java classfiles (2016)
  19. Bob Carpenter, Matthew D. Hoffman, Marcus Brubaker, Daniel Lee, Peter Li, Michael Betancourt: The Stan Math Library: Reverse-Mode Automatic Differentiation in C++ (2015) arXiv
  20. Berthold, Timo: RENS. The optimal rounding (2014)

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