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ClpLsqr Class Reference

This class implements LSQR. More...

#include <ClpLsqr.hpp>

+ Collaboration diagram for ClpLsqr:

Public Member Functions

Constructors and destructors
 ClpLsqr ()
 Default constructor. More...
 
 ClpLsqr (ClpInterior *model)
 Constructor for use with Pdco model (note modified for pdco!!!!) More...
 
 ClpLsqr (const ClpLsqr &)
 Copy constructor. More...
 
ClpLsqroperator= (const ClpLsqr &rhs)
 Assignment operator. This copies the data. More...
 
 ~ClpLsqr ()
 Destructor. More...
 
Methods
bool setParam (char *parmName, int parmValue)
 Set an int parameter. More...
 
void do_lsqr (CoinDenseVector< double > &b, double damp, double atol, double btol, double conlim, int itnlim, bool show, Info info, CoinDenseVector< double > &x, int *istop, int *itn, Outfo *outfo, bool precon, CoinDenseVector< double > &Pr)
 Call the Lsqr algorithm. More...
 
void matVecMult (int, CoinDenseVector< double > *, CoinDenseVector< double > *)
 Matrix-vector multiply - implemented by user. More...
 
void matVecMult (int, CoinDenseVector< double > &, CoinDenseVector< double > &)
 
void borrowDiag1 (double *array)
 diag1 - we just borrow as it is part of a CoinDenseVector<double> More...
 

Public Attributes

Public member data
int nrows_
 Row dimension of matrix. More...
 
int ncols_
 Column dimension of matrix. More...
 
ClpInteriormodel_
 Pointer to Model object for this instance. More...
 
double * diag1_
 Diagonal array 1. More...
 
double diag2_
 Constant diagonal 2. More...
 

Detailed Description

This class implements LSQR.

 LSQR solves  Ax = b  or  min ||b - Ax||_2  if damp = 0,
 or   min || (b)  -  (  A   )x ||   otherwise.
          || (0)     (damp I)  ||2
 A  is an m by n matrix defined by user provided routines
 matVecMult(mode, y, x)
 which performs the matrix-vector operations where y and x
 are references or pointers to CoinDenseVector objects.
 If mode = 1, matVecMult  must return  y = Ax   without altering x.
 If mode = 2, matVecMult  must return  y = A'x  without altering x.

-----------------------------------------------------------------------
 LSQR uses an iterative (conjugate-gradient-like) method.
 For further information, see
 1. C. C. Paige and M. A. Saunders (1982a).
    LSQR: An algorithm for sparse linear equations and sparse least squares,
    ACM TOMS 8(1), 43-71.
 2. C. C. Paige and M. A. Saunders (1982b).
    Algorithm 583.  LSQR: Sparse linear equations and least squares problems,
    ACM TOMS 8(2), 195-209.
 3. M. A. Saunders (1995).  Solution of sparse rectangular systems using
    LSQR and CRAIG, BIT 35, 588-604.

 Input parameters:

 atol, btol  are stopping tolerances.  If both are 1.0e-9 (say),
             the final residual norm should be accurate to about 9 digits.
             (The final x will usually have fewer correct digits,
             depending on cond(A) and the size of damp.)
 conlim      is also a stopping tolerance.  lsqr terminates if an estimate
             of cond(A) exceeds conlim.  For compatible systems Ax = b,
             conlim could be as large as 1.0e+12 (say).  For least-squares
             problems, conlim should be less than 1.0e+8.
             Maximum precision can be obtained by setting
             atol = btol = conlim = zero, but the number of iterations
             may then be excessive.
 itnlim      is an explicit limit on iterations (for safety).
 show = 1    gives an iteration log,
 show = 0    suppresses output.
 info        is a structure special to pdco.m, used to test if
             was small enough, and continuing if necessary with smaller atol.


 Output parameters:
 x           is the final solution.
 *istop       gives the reason for termination.
 *istop       = 1 means x is an approximate solution to Ax = b.
             = 2 means x approximately solves the least-squares problem.
 rnorm       = norm(r) if damp = 0, where r = b - Ax,
             = sqrt( norm(r)**2  +  damp**2 * norm(x)**2 ) otherwise.
 xnorm       = norm(x).
 var         estimates diag( inv(A'A) ).  Omitted in this special version.
 outfo       is a structure special to pdco.m, returning information
             about whether atol had to be reduced.

 Other potential output parameters:
 anorm, acond, arnorm, xnorm

Definition at line 76 of file ClpLsqr.hpp.

Constructor & Destructor Documentation

ClpLsqr::ClpLsqr ( )

Default constructor.

ClpLsqr::ClpLsqr ( ClpInterior model)

Constructor for use with Pdco model (note modified for pdco!!!!)

ClpLsqr::ClpLsqr ( const ClpLsqr )

Copy constructor.

ClpLsqr::~ClpLsqr ( )

Destructor.

Member Function Documentation

ClpLsqr& ClpLsqr::operator= ( const ClpLsqr rhs)

Assignment operator. This copies the data.

bool ClpLsqr::setParam ( char *  parmName,
int  parmValue 
)

Set an int parameter.

void ClpLsqr::do_lsqr ( CoinDenseVector< double > &  b,
double  damp,
double  atol,
double  btol,
double  conlim,
int  itnlim,
bool  show,
Info  info,
CoinDenseVector< double > &  x,
int *  istop,
int *  itn,
Outfo outfo,
bool  precon,
CoinDenseVector< double > &  Pr 
)

Call the Lsqr algorithm.

void ClpLsqr::matVecMult ( int  ,
CoinDenseVector< double > *  ,
CoinDenseVector< double > *   
)

Matrix-vector multiply - implemented by user.

void ClpLsqr::matVecMult ( int  ,
CoinDenseVector< double > &  ,
CoinDenseVector< double > &   
)
void ClpLsqr::borrowDiag1 ( double *  array)
inline

diag1 - we just borrow as it is part of a CoinDenseVector<double>

Definition at line 125 of file ClpLsqr.hpp.

Member Data Documentation

int ClpLsqr::nrows_

Row dimension of matrix.

Definition at line 86 of file ClpLsqr.hpp.

int ClpLsqr::ncols_

Column dimension of matrix.

Definition at line 88 of file ClpLsqr.hpp.

ClpInterior* ClpLsqr::model_

Pointer to Model object for this instance.

Definition at line 90 of file ClpLsqr.hpp.

double* ClpLsqr::diag1_

Diagonal array 1.

Definition at line 92 of file ClpLsqr.hpp.

double ClpLsqr::diag2_

Constant diagonal 2.

Definition at line 94 of file ClpLsqr.hpp.


The documentation for this class was generated from the following file: