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I am trying to solve some nonlinear systems by Newton's method and the solution accuracy is very important to my problem.

  1. without using the symbolic computation softwares, how can I compute the Jacobian of a general nonlinear system of polynomials via C++ or other similar programming languages? The difficulties for me are mainly:

    • as accurate as symbolic Jacobian
    • an algorithm suitable for general nonlinear system cases
    • only dependent on C++ or similar programming languages;

  2. If I have to use finite difference method to obtain an approximate Jacobian, how the step size chosen would affect the final solution accuracy? how to determine the step size so that I can obtain best solution accuracy under the same computation precision level? How to determine (quantitatively) the effects of approximate Jacobian on the accuracy of the final solution?

gunr2171
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LCFactorization
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2 Answers2

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Check out the idea of dual numbers, there exist several example implementations in C++. Evaluating the functions using appropriately initialized dual numbers results in the evaluation of one directional derivative. Repeat this for all coordinate directions.

For a nice introduction, see Piponi: AD, C++ and Photogrammetry (http://el.mdu.edu.tw/datacos/09820722022O/paper.pdf)

If L is the effort to evaluate the functions, one directional derivative costs about 3*L, the full jacobian 3n*L. If one were to combine all the directions in one evaluation, this reduces to (1+2n)*L. But by then we have entered the realm of automatic or algorithmic differentiation.

Look for FADBAD/TADIFF for an easy implementation, for really fast codes one uses code transformations as provided by the Tapenade project. ADOL-C is in between, more automatic than Tapenade, faster than FADBAD.

Lutz Lehmann
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I solved the problem myself by using finite difference approximation of Jacobian;

It seems there is essentially no difference when solving a nonlinear system via symbolic Jacobian or finite difference Jacobian in either efficiency or accuracy.

The only difference is: symbolic Jacobian requires less step to reach desired convergence; if Jacobian can be provided, then it will be faster.

LCFactorization
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    There are other advantages. With blind finite-difference schemes it can be difficult to take advantage of sparsity (e.g. many wasted function evaluations), whereas if a symbolic Jacobian is available then it will obviously expose the sparsity directly. – Caleb Hattingh Feb 27 '14 at 00:39