spfactor.c

spice中支持多层次元件模型仿真的可单独运行的插件源码

 *      Pointer to matrix.
 *
 *  >>> Local variables:
 *  SizePlusOne  (unsigned)
 *      Size of the arrays to be allocated.
 *
 *  >>> Possible errors:
 *  spNO_MEMORY
 */

void
spcCreateInternalVectors( Matrix )

MatrixPtr  Matrix;
{
int  Size;

/* Begin `spcCreateInternalVectors'. */
/* Create Markowitz arrays. */
    Size= Matrix->Size;

    if (Matrix->MarkowitzRow == NULL)
    {   if (( Matrix->MarkowitzRow = ALLOC(int, Size+1)) == NULL)
            Matrix->Error = spNO_MEMORY;
    }
    if (Matrix->MarkowitzCol == NULL)
    {   if (( Matrix->MarkowitzCol = ALLOC(int, Size+1)) == NULL)
            Matrix->Error = spNO_MEMORY;
    }
    if (Matrix->MarkowitzProd == NULL)
    {   if (( Matrix->MarkowitzProd = ALLOC(long, Size+2)) == NULL)
            Matrix->Error = spNO_MEMORY;
    }

/* Create DoDirect vectors for use in spFactor(). */
#if REAL
    if (Matrix->DoRealDirect == NULL)
    {   if (( Matrix->DoRealDirect = ALLOC(BOOLEAN, Size+1)) == NULL)
            Matrix->Error = spNO_MEMORY;
    }
#endif
#if spCOMPLEX
    if (Matrix->DoCmplxDirect == NULL)
    {   if (( Matrix->DoCmplxDirect = ALLOC(BOOLEAN, Size+1)) == NULL)
            Matrix->Error = spNO_MEMORY;
    }
#endif

/* Create Intermediate vectors for use in MatrixSolve. */
#if spCOMPLEX
    if (Matrix->Intermediate == NULL)
    {   if ((Matrix->Intermediate = ALLOC(RealNumber,2*(Size+1))) == NULL)
            Matrix->Error = spNO_MEMORY;
    }
#else
    if (Matrix->Intermediate == NULL)
    {   if ((Matrix->Intermediate = ALLOC(RealNumber, Size+1)) == NULL)
            Matrix->Error = spNO_MEMORY;
    }
#endif

    if (Matrix->Error != spNO_MEMORY)
        Matrix->InternalVectorsAllocated = YES;
    return;
}







/*
 *  COUNT MARKOWITZ
 *
 *  Scans Matrix to determine the Markowitz counts for each row and column.
 *
 *  >>> Arguments:
 *  Matrix  <input>  (MatrixPtr)
 *      Pointer to matrix.
 *  RHS  <input>  (RealVector)
 *      Representative right-hand side vector that is used to determine
 *      pivoting order when the right hand side vector is sparse.  If
 *      RHS is a NULL pointer then the RHS vector is assumed to be full
 *      and it is not used when determining the pivoting order.
 *  Step  <input>  (int)
 *     Index of the diagonal currently being eliminated.
 *
 *  >>> Local variables:
 *  Count  (int)
 *     Temporary counting variable.
 *  ExtRow  (int)
 *     The external row number that corresponds to I.
 *  pElement  (ElementPtr)
 *     Pointer to matrix elements.
 *  Size  (int)
 *     The size of the matrix.
 */

static void
CountMarkowitz( Matrix, RHS, Step )

MatrixPtr Matrix;
register RealVector  RHS;
int Step;
{
register int  Count, I, Size = Matrix->Size;
register ElementPtr  pElement;
int  ExtRow;

/* Begin `CountMarkowitz'. */

/* Correct array pointer for ARRAY_OFFSET. */
#if NOT ARRAY_OFFSET
#if spSEPARATED_COMPLEX_VECTORS OR NOT spCOMPLEX
        if (RHS != NULL) --RHS;
#else
        if (RHS != NULL)
        {   if (Matrix->Complex) RHS -= 2;
            else --RHS;
        }
#endif
#endif

/* Generate MarkowitzRow Count for each row. */
    for (I = Step; I <= Size; I++)
    {
/* Set Count to -1 initially to remove count due to pivot element. */
        Count = -1;
        pElement = Matrix->FirstInRow[I];
        while (pElement != NULL AND pElement->Col < Step)
            pElement = pElement->NextInRow;
        while (pElement != NULL)
        {   Count++;
            pElement = pElement->NextInRow;
        }

/* Include nonzero elements in the RHS vector. */
        ExtRow = Matrix->IntToExtRowMap[I];

#if spSEPARATED_COMPLEX_VECTORS OR NOT spCOMPLEX
        if (RHS != NULL)
            if (RHS[ExtRow] != 0.0)  Count++;
#else
        if (RHS != NULL)
        {   if (Matrix->Complex)
            {   if ((RHS[2*ExtRow] != 0.0) OR (RHS[2*ExtRow+1] != 0.0))
                    Count++;
            }
            else if (RHS[I] != 0.0) Count++;
        }
#endif
        Matrix->MarkowitzRow[I] = Count;
    }

/* Generate the MarkowitzCol count for each column. */
    for (I = Step; I <= Size; I++)
    {
/* Set Count to -1 initially to remove count due to pivot element. */
        Count = -1;
        pElement = Matrix->FirstInCol[I];
        while (pElement != NULL AND pElement->Row < Step)
            pElement = pElement->NextInCol;
        while (pElement != NULL)
        {   Count++;
            pElement = pElement->NextInCol;
        }
        Matrix->MarkowitzCol[I] = Count;
    }
    return;
}










/*
 *  MARKOWITZ PRODUCTS
 *
 *  Calculates MarkowitzProduct for each diagonal element from the Markowitz
 *  counts.
 *
 *  >>> Arguments:
 *  Matrix  <input>  (MatrixPtr)
 *      Pointer to matrix.
 *  Step  <input>  (int)
 *      Index of the diagonal currently being eliminated.
 *
 *  >>> Local Variables:
 *  pMarkowitzProduct  (long *)
 *      Pointer that points into MarkowitzProduct array. Is used to
 *      sequentially access entries quickly.
 *  pMarkowitzRow  (int *)
 *      Pointer that points into MarkowitzRow array. Is used to sequentially
 *      access entries quickly.
 *  pMarkowitzCol  (int *)
 *      Pointer that points into MarkowitzCol array. Is used to sequentially
 *      access entries quickly.
 *  Product  (long)
 *      Temporary storage for Markowitz product./
 *  Size  (int)
 *      The size of the matrix.
 */

static void
MarkowitzProducts( Matrix, Step )

MatrixPtr Matrix;
int Step;
{
register  int  I, *pMarkowitzRow, *pMarkowitzCol;
register  long  Product, *pMarkowitzProduct;
register  int  Size = Matrix->Size;
double fProduct;

/* Begin `MarkowitzProducts'. */
    Matrix->Singletons = 0;

    pMarkowitzProduct = &(Matrix->MarkowitzProd[Step]);
    pMarkowitzRow = &(Matrix->MarkowitzRow[Step]);
    pMarkowitzCol = &(Matrix->MarkowitzCol[Step]);

    for (I = Step; I <= Size; I++)
    {
/* If chance of overflow, use real numbers. */
        if ((*pMarkowitzRow > LARGEST_SHORT_INTEGER AND *pMarkowitzCol != 0) OR
            (*pMarkowitzCol > LARGEST_SHORT_INTEGER AND *pMarkowitzRow != 0))
        {   fProduct = (double)(*pMarkowitzRow++) * (double)(*pMarkowitzCol++);
            if (fProduct >= LARGEST_LONG_INTEGER)
                *pMarkowitzProduct++ = LARGEST_LONG_INTEGER;
            else
                *pMarkowitzProduct++ = fProduct;
        }
        else
        {   Product = *pMarkowitzRow++ * *pMarkowitzCol++;
            if ((*pMarkowitzProduct++ = Product) == 0)
                Matrix->Singletons++;
        }
    }
    return;
}











/*
 *  SEARCH FOR BEST PIVOT
 *
 *  Performs a search to determine the element with the lowest Markowitz
 *  Product that is also acceptable.  An acceptable element is one that is
 *  larger than the AbsThreshold and at least as large as RelThreshold times
 *  the largest element in the same column.  The first step is to look for
 *  singletons if any exist.  If none are found, then all the diagonals are
 *  searched. The diagonal is searched once quickly using the assumption that
 *  elements on the diagonal are large compared to other elements in their
 *  column, and so the pivot can be chosen only on the basis of the Markowitz
 *  criterion.  After a element has been chosen to be pivot on the basis of
 *  its Markowitz product, it is checked to see if it is large enough.
 *  Waiting to the end of the Markowitz search to check the size of a pivot
 *  candidate saves considerable time, but is not guaranteed to find an
 *  acceptable pivot.  Thus if unsuccessful a second pass of the diagonal is
 *  made.  This second pass checks to see if an element is large enough during
 *  the search, not after it.  If still no acceptable pivot candidate has
 *  been found, the search expands to cover the entire matrix.
 *
 *  >>> Returned:
 *  A pointer to the element chosen to be pivot.  If every element in the
 *  matrix is zero, then NULL is returned.
 *
 *  >>> Arguments:
 *  Matrix  <input>  (MatrixPtr)
 *      Pointer to matrix.
 *  Step  <input>  (int)
 *      The row and column number of the beginning of the reduced submatrix.
 *
 *  >>> Local variables:
 *  ChosenPivot  (ElementPtr)
 *      Pointer to element that has been chosen to be the pivot.
 *
 *  >>> Possible errors:
 *  spSINGULAR
 *  spSMALL_PIVOT
 */

static ElementPtr
SearchForPivot( Matrix, Step, DiagPivoting )

MatrixPtr Matrix;
int Step, DiagPivoting;
{
register ElementPtr  ChosenPivot;
ElementPtr  SearchForSingleton();
ElementPtr  QuicklySearchDiagonal();
ElementPtr  SearchDiagonal();
ElementPtr  SearchEntireMatrix();

/* Begin `SearchForPivot'. */

/* If singletons exist, look for an acceptable one to use as pivot. */
    if (Matrix->Singletons)
    {   ChosenPivot = SearchForSingleton( Matrix, Step );
        if (ChosenPivot != NULL)
        {   Matrix->PivotSelectionMethod = 's';
            return ChosenPivot;
        }
    }

#if DIAGONAL_PIVOTING
    if (DiagPivoting)
    {
/*
 * Either no singletons exist or they weren't acceptable.  Take quick first
 * pass at searching diagonal.  First search for element on diagonal of 
 * remaining submatrix with smallest Markowitz product, then check to see
 * if it okay numerically.  If not, QuicklySearchDiagonal fails.
 */
        ChosenPivot = QuicklySearchDiagonal( Matrix, Step );
        if (ChosenPivot != NULL)
        {   Matrix->PivotSelectionMethod = 'q';
            return ChosenPivot;
        }

/*
 * Quick search of diagonal failed, carefully search diagonal and check each
 * pivot candidate numerically before even tentatively accepting it.
 */
        ChosenPivot = SearchDiagonal( Matrix, Step );
        if (ChosenPivot != NULL)
        {   Matrix->PivotSelectionMethod = 'd';
            return ChosenPivot;
        }
    }
#endif /* DIAGONAL_PIVOTING */

/* No acceptable pivot found yet, search entire matrix. */
    ChosenPivot = SearchEntireMatrix( Matrix, Step );
    Matrix->PivotSelectionMethod = 'e';

    return ChosenPivot;
}









/*
 *  SEARCH FOR SINGLETON TO USE AS PIVOT
 *
 *  Performs a search to find a singleton to use as the pivot.  The
 *  first acceptable singleton is used.  A singleton is acceptable if
 *  it is larger in magnitude than the AbsThreshold and larger
 *  than RelThreshold times the largest of any other elements in the same
 *  column.  It may seem that a singleton need not satisfy the
 *  relative threshold criterion, however it is necessary to prevent
 *  excessive growth in the RHS from resulting in overflow during the
 *  forward and backward substitution.  A singleton does not need to
 *  be on the diagonal to be selected.
 *
 *  >>> Returned:
 *  A pointer to the singleton chosen to be pivot.  In no singleton is
 *  acceptable, return NULL.
 *
 *  >>> Arguments:
 *  Matrix  <input>  (MatrixPtr)
 *      Pointer to matrix.
 *  Step  <input>  (int)