cisst-saw
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#include <nmrLSNonLinSolver.h>
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nmrLSNonLinSolver (void) | |
nmrLSNonLinSolver (CISSTNETLIB_INTEGER m, CISSTNETLIB_INTEGER n) | |
void | Allocate (CISSTNETLIB_INTEGER m, CISSTNETLIB_INTEGER n) |
nmrLSNonLinSolver (vctDynamicVector< CISSTNETLIB_DOUBLE > &X, vctDynamicVector< CISSTNETLIB_DOUBLE > &F) | |
void | Allocate (vctDynamicVector< CISSTNETLIB_DOUBLE > &X, vctDynamicVector< CISSTNETLIB_DOUBLE > &F) |
template<int __instanceLine, class __elementType > | |
void | Solve (nmrCallBackFunctionF< __instanceLine, __elementType > &callBack, vctDynamicVector< CISSTNETLIB_DOUBLE > &X, vctDynamicVector< CISSTNETLIB_DOUBLE > &F, CISSTNETLIB_DOUBLE tolerance) throw (std::runtime_error) |
Protected Attributes | |
CISSTNETLIB_INTEGER | M |
CISSTNETLIB_INTEGER | N |
CISSTNETLIB_DOUBLE | Tolerance |
CISSTNETLIB_INTEGER | Info |
CISSTNETLIB_INTEGER | Lwork |
vctDynamicVector < CISSTNETLIB_INTEGER > | IWork |
vctDynamicVector < CISSTNETLIB_DOUBLE > | Work |
Algorithm LSNonLin: Non Linear Least Squares by Levenberg Marquardt method The purpose of this is to minimize the sum of the squares of M nonlinear functions in N variables by a modification of the levenberg-marquardt algorithm. The user must provide a subroutine which calculates the functions. The jacobian is then calculated by a forward-difference approximation.
Rather than compute the value f(x) (the sum of squares), LSNonLin requires the user-defined function to compute the vector-valued function. Then, in vector terms, you can restate this optimization problem as
where
is a vector and
is a function that returns a vector value.
The data members of this class are:
info = 0 improper input parameters. info = 1 algorithm estimates that the relative error in the sum of squares is at most tol. info = 2 algorithm estimates that the relative error between x and the solution is at most tol. info = 3 conditions for info = 1 and info = 2 both hold. info = 4 fvec is orthogonal to the columns of the jacobian to machine precision. info = 5 number of calls to fcn has reached or exceeded 200*(n+1). info = 6 tol is too small. no further reduction in the sum of squares is possible. info = 7 tol is too small. no further improvement in the approximate solution x is possible
The input/output from this class is:
callBack: Is object of type nmrCallBackLSNonLinSolver used to supply the user method. The user method which belongs to a user defined class 'Cfoo' has the following definition int Cfoo::Mbar (vctDynamicVectorRef<CISSTNETLIB_DOUBLE> &X, vctDynamicVectorRef<CISSTNETLIB_DOUBLE> &F, CISSTNETLIB_INTEGER &Flag); The solver calls this method when needed to obtain values for F for a given variable values X. the value of Flag should not be changed by Mbar unless the user wants to terminate execution of Solver. in this case set Flag to a negative integer.
The following constructor can be used to contruct the nmrCallBackLSNonLinSolver object required to be passed to the Solve() method. This object needs to be created only once per given set of nonlinear functions to be minized. nmrCallBackLSNonLinSolver<nmrUNIQUE_IDENTIFIER_LINE, Cfoo> callBackObject(this, &Cfoo::Mbar);
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Default constructor. This constructor doesn't allocate any memory. If you use this constructor, you will need to use one of the Allocate() methods before you can use the Solve method.
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Constructor with memory allocation. This constructor allocates the memory based on M and N. It relies on the method Allocate(). The next call to the Solve() method will check that the parameters match the dimension.
m | Number of nonlinear functions. |
n | Number of variables This order will be used for the output as well. |
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Constructor with memory allocation. This constructor allocates the memory based on the actual input of the Solve() method. It relies on the method Allocate(). The next call to the Solve() method will check that the parameters match the dimension and storage order.
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This method allocates the memory based on M and N. The next call to the Solve() method will check that the parameters match the dimension.
m | Number of nonlinear functions |
n | Number of variables |
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Allocate memory to solve this problem. This method provides a convenient way to extract the required sizes from the input containers. The next call to the Solve() method will check that the parameters match the dimension.
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This computes the solves nonlinear least squares problem invloving M functions in N variables. On input, X contains the starting point, On output, X contains the final estimate, F contrains the final value
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