ADMAT
ADMAT 2.0 Many scientific computing tasks require the repeated computation of derivatives. Hand-coding of derivative functions can be tedious, complex, and error-prone. Moreover, the computation of first and second derivatives, and sometimes the Newton step, is often a dominant step in a scientific computing code. Derivative approximations such as finite-differencing involve additional errors and heuristic choice of parameters. This toolbox is designed to help a MATLAB user compute first and second derivatives and related structures efficiently, accurately, and automatically. ADMAT 2.0 employs many sophisticated techniques such as exploiting sparsity and structure to achieve high efficiency in computing derivative structures including gradients, Jacobians, and Hessians. Moreover, ADMAT 2.0 can directly calculate Newton steps for nonlinear systems, often with great efficiency. A MATLAB user needs only to provide an M-file that evaluates a smooth nonlinear objective function at a given point. On request and when appropriate, ADMAT 2.0 will evaluate the Jacobian matrix (for which the gradient is a special case), the Hessian matrix, and possibly the Newton step in addition to the evaluation of the objective function at the given point. There is no need for the user to provide code for derivative calculation or an approximation scheme. Please use the following link to download ADMAT 2.0 from us. You may try ADMAT 2.0 free of charge for up to one year.
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References in zbMATH (referenced in 26 articles )
Showing results 1 to 20 of 26.
Sorted by year (- Xu, Wei; Chen, Yuehuan; Coleman, Conrad; Coleman, Thomas F.: Moment matching machine learning methods for risk management of large variable annuity portfolios (2018)
- Coleman, Thomas F.; Xu, Wei: Automatic differentiation in MATLAB using ADMAT with applications (2016)
- Sluşanschi, Emil I.; Dumitrel, Vlad: ADiJaC -- automatic differentiation of Java classfiles (2016)
- Xu, Wei; Zheng, Ning; Hayami, Ken: Jacobian-free implicit inner-iteration preconditioner for nonlinear least squares problems (2016)
- Xu, Wei; Coleman, Thomas F.: Solving nonlinear equations with the Newton-Krylov method based on automatic differentiation (2014)
- Patterson, Michael A.; Weinstein, Matthew; Rao, Anil V.: An efficient overloaded method for computing derivatives of mathematical functions in MATLAB (2013)
- Xu, Wei; Coleman, Thomas F.: Efficient (partial) determination of derivative matrices via automatic differentiation (2013)
- Andersson, Joel; Åkesson, Johan; Diehl, Moritz: CasADi: a symbolic package for automatic differentiation and optimal control (2012)
- Coleman, Thomas F.; Xiong, Xin; Xu, Wei: Using directed edge separators to increase efficiency in the determination of Jacobian matrices via automatic differentiation (2012)
- Gáti, Attila: Miller analyzer for Matlab: a Matlab package for automatic roundoff analysis (2012)
- Xu, Wei; Coleman, Thomas F.; Liu, Gang: A secant method for nonlinear least-squares minimization (2012)
- De Witte, Virginie; Govaerts, Willy: Numerical computation of normal form coefficients of bifurcations of ODEs in \textscMatlab (2011)
- Lampoh, Komlanvi; Charpentier, Isabelle; Daya, El Mostafa: A generic approach for the solution of nonlinear residual equations. III: Sensitivity computations (2011)
- 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)
- Giles, Mike B.: Collected matrix derivative results for forward and reverse mode algorithmic differentiation (2008)
- Koutsawa, Yao; Charpentier, Isabelle; Daya, El Mostafa; Cherkaoui, Mohammed: A generic approach for the solution of nonlinear residual equations. I: The Diamant toolbox (2008)
- Noack, Antje; Walther, Andrea: Adjoint concepts for the optimal control of Burgers equation (2007)
- Dussault, Jean-Pierre; Hamelin, Benoit: Robust descent in differentiable optimization using automatic finite differences (2006)
- Kharche, Rahul V.; Forth, Shaun A.: Source transformation for MATLAB automatic differentiation (2006)
- 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)