ADMIT-1. Automatic differentiation and MATLAB interface toolbox. ADMIT-1 enables the computation of sparse Jacobian and Hessian matrices, using automatic differentiation technology, from a MATLAB environment. Given a function to be differentiated, ADMIT-1 will exploit sparsity if present to yield sparse derivative matrices (in sparse MATLAB form). A generic automatic differentiation tool, subject to some functionality requirements, can be plugged into ADMIT-1; examples include ADOL-C (C/C++ target functions)and ADMAT (MATLAB target funcitons). ADMIT-1 also allows for the calculation of gradients and has several other related functions. This article provides an introduction to the design and usage of ADMIT-1.

References in zbMATH (referenced in 20 articles , 1 standard article )

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  1. Moazeni, Somayeh; Coleman, Thomas F.; Li, Yuying: Smoothing and parametric rules for stochastic mean-CVaR optimal execution strategy (2016)
  2. Patterson, Michael A.; Weinstein, Matthew; Rao, Anil V.: An efficient overloaded method for computing derivatives of mathematical functions in MATLAB (2013)
  3. Lülfesmann, Michael: Full and partial Jacobian computation vie graph coloring: Algorithms and applications. (2012)
  4. De Witte, Virginie; Govaerts, Willy: Numerical computation of normal form coefficients of bifurcations of ODEs in Matlab (2011)
  5. Jian, Jin-Bao; Tang, Chun-Ming; Zheng, Hai-Yan: Sequential quadratically constrained quadratic programming norm-relaxed algorithm of strongly sub-feasible directions (2010)
  6. Pryce, J.D.; Ghaziani, R.Khoshsiar; De Witte, V.; Govaerts, W.: Computation of normal form coefficients of cycle bifurcations of maps by algorithmic differentiation (2010)
  7. Alexe, Mihai; Sandu, Adrian: On the discrete adjoints of adaptive time stepping algorithms (2009)
  8. Bischof, Christian H.; Hovland, Paul D.; Norris, Boyana: On the implementation of automatic differentiation tools (2008)
  9. Bücker, H.Martin; Vehreschild, Andre: Coping with a variable number of arguments when transforming MATLAB programs (2008)
  10. Giles, Mike B.: Collected matrix derivative results for forward and reverse mode algorithmic differentiation (2008)
  11. Abokhodair, Abdulwahab A.: Numerical tools for geoscience computations: semiautomatic differentiation-SD (2007)
  12. Dussault, Jean-Pierre; Hamelin, Benoit: Robust descent in differentiable optimization using automatic finite differences (2006)
  13. Shampine, L.F.; Ketzscher, Robert; Forth, Shaun A.: Using AD to solve BVPs in MATLAB (2005)
  14. Bischof, Christian H.; Bücker, H.Martin; Rasch, Arno: Sensitivity analysis of turbulence models using automatic differentiation (2004)
  15. Forth, Shaun A.; Tadjouddine, Mohamed; Pryce, John D.; Reid, John K.: Jacobian code generated by source transformation and vertex elimination can be as efficient as hand-coding (2004)
  16. Bischof, Christian; Lang, Bruno; Vehreschild, Andre: Automatic differentiation for MATLAB programs (2003)
  17. Anitescu, Mihai: A superlinearly convergent sequential quadratically constrained quadratic programming algorithm for degenerate nonlinear programming (2002)
  18. Borggaard, Jeff; Verma, Arun: On efficient solutions to the continuous sensitivity equation using automatic differentiation (2000)
  19. Coleman, Thomas F.; Verma, Arun: ADMIT-1. Automatic differentiation and MATLAB interface toolbox. (2000)
  20. Coleman, Thomas F.; Jonsson, Gudbjorn F.: The efficient computation of structured gradients using automatic differentiation (1999)