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/* ----------------------------------------------------------------------
* Project: CMSIS DSP Library
* Title: arm_mat_inverse_f32.c
* Description: Floating-point matrix inverse
*
* $Date: 23 April 2021
* $Revision: V1.9.0
*
* Target Processor: Cortex-M and Cortex-A cores
* -------------------------------------------------------------------- */
/*
* Copyright (C) 2010-2021 ARM Limited or its affiliates. All rights reserved.
*
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the License); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* [url]www.apache.org/licenses/LICENSE-2.0[/url]
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "dsp/matrix_functions.h"
#include "dsp/matrix_utils.h"
/**
@ingroup groupMatrix
*/
/**
@defgroup MatrixInv Matrix Inverse
Computes the inverse of a matrix.
The inverse is defined only if the input matrix is square and non-singular (the determinant is non-zero).
The function checks that the input and output matrices are square and of the same size.
Matrix inversion is numerically sensitive and the CMSIS DSP library only supports matrix
inversion of floating-point matrices.
@par Algorithm
The Gauss-Jordan method is used to find the inverse.
The algorithm performs a sequence of elementary row-operations until it
reduces the input matrix to an identity matrix. Applying the same sequence
of elementary row-operations to an identity matrix yields the inverse matrix.
If the input matrix is singular, then the algorithm terminates and returns error status
<code>ARM_MATH_SINGULAR</code>.
\image html MatrixInverse.gif "Matrix Inverse of a 3 x 3 matrix using Gauss-Jordan Method"
*/
/**
@addtogroup MatrixInv
@{
*/
/**
@brief Floating-point matrix inverse.
@param[in] pSrc points to input matrix structure. The source matrix is modified by the function.
@param[out] pDst points to output matrix structure
@return execution status
- \ref ARM_MATH_SUCCESS : Operation successful
- \ref ARM_MATH_SIZE_MISMATCH : Matrix size check failed
- \ref ARM_MATH_SINGULAR : Input matrix is found to be singular (non-invertible)
*/
arm_status arm_mat_inverse_f32(
const arm_matrix_instance_f32 * pSrc,
arm_matrix_instance_f32 * pDst)
{
float32_t *pIn = pSrc->pData; /* input data matrix pointer */
float32_t *pOut = pDst->pData; /* output data matrix pointer */
float32_t *pTmp;
uint32_t numRows = pSrc->numRows; /* Number of rows in the matrix */
uint32_t numCols = pSrc->numCols; /* Number of Cols in the matrix */
float32_t pivot = 0.0f, newPivot=0.0f; /* Temporary input values */
uint32_t selectedRow,pivotRow,i, rowNb, rowCnt, flag = 0U, j,column; /* loop counters */
arm_status status; /* status of matrix inverse */
#ifdef ARM_MATH_MATRIX_CHECK
/* Check for matrix mismatch condition */
if ((pSrc->numRows != pSrc->numCols) ||
(pDst->numRows != pDst->numCols) ||
(pSrc->numRows != pDst->numRows) )
{
/* Set status as ARM_MATH_SIZE_MISMATCH */
status = ARM_MATH_SIZE_MISMATCH;
}
else
#endif /* #ifdef ARM_MATH_MATRIX_CHECK */
{
/*--------------------------------------------------------------------------------------------------------------
* Matrix Inverse can be solved using elementary row operations.
*
* Gauss-Jordan Method:
*
* 1. First combine the identity matrix and the input matrix separated by a bar to form an
* augmented matrix as follows:
* _ _ _ _
* | a11 a12 | 1 0 | | X11 X12 |
* | | | = | |
* |_ a21 a22 | 0 1 _| |_ X21 X21 _|
*
* 2. In our implementation, pDst Matrix is used as identity matrix.
*
* 3. Begin with the first row. Let i = 1.
*
* 4. Check to see if the pivot for row i is zero.
* The pivot is the element of the main diagonal that is on the current row.
* For instance, if working with row i, then the pivot element is aii.
* If the pivot is zero, exchange that row with a row below it that does not
* contain a zero in column i. If this is not possible, then an inverse
* to that matrix does not exist.
*
* 5. Divide every element of row i by the pivot.
*
* 6. For every row below and row i, replace that row with the sum of that row and
* a multiple of row i so that each new element in column i below row i is zero.
*
* 7. Move to the next row and column and repeat steps 2 through 5 until you have zeros
* for every element below and above the main diagonal.
*
* 8. Now an identical matrix is formed to the left of the bar(input matrix, pSrc).
* Therefore, the matrix to the right of the bar is our solution(pDst matrix, pDst).
*----------------------------------------------------------------------------------------------------------------*/
/* Working pointer for destination matrix */
pTmp = pOut;
/* Loop over the number of rows */
rowCnt = numRows;
/* Making the destination matrix as identity matrix */
while (rowCnt > 0U)
{
/* Writing all zeroes in lower triangle of the destination matrix */
j = numRows - rowCnt;
while (j > 0U)
{
*pTmp++ = 0.0f;
j--;
}
/* Writing all ones in the diagonal of the destination matrix */
*pTmp++ = 1.0f;
/* Writing all zeroes in upper triangle of the destination matrix */
j = rowCnt - 1U;
while (j > 0U)
{
*pTmp++ = 0.0f;
j--;
}
/* Decrement loop counter */
rowCnt--;
}
/* Loop over the number of columns of the input matrix.
All the elements in each column are processed by the row operations */
/* Index modifier to navigate through the columns */
for(column = 0U; column < numCols; column++)
{
/* Check if the pivot element is zero..
* If it is zero then interchange the row with non zero row below.
* If there is no non zero element to replace in the rows below,
* then the matrix is Singular. */
pivotRow = column;
/* Temporary variable to hold the pivot value */
pTmp = ELEM(pSrc,column,column) ;
pivot = *pTmp;
selectedRow = column;
/* Find maximum pivot in column */
/* Loop over the number rows present below */
for (rowNb = column+1; rowNb < numRows; rowNb++)
{
/* Update the input and destination pointers */
pTmp = ELEM(pSrc,rowNb,column);
newPivot = *pTmp;
if (fabsf(newPivot) > fabsf(pivot))
{
selectedRow = rowNb;
pivot = newPivot;
}
}
/* Check if there is a non zero pivot element to
* replace in the rows below */
if ((pivot != 0.0f) && (selectedRow != column))
{
SWAP_ROWS_F32(pSrc,column, pivotRow,selectedRow);
SWAP_ROWS_F32(pDst,0, pivotRow,selectedRow);
/* Flag to indicate whether exchange is done or not */
flag = 1U;
}
/* Update the status if the matrix is singular */
if ((flag != 1U) && (pivot == 0.0f))
{
return ARM_MATH_SINGULAR;
}
/* Pivot element of the row */
pivot = 1.0f / pivot;
SCALE_ROW_F32(pSrc,column,pivot,pivotRow);
SCALE_ROW_F32(pDst,0,pivot,pivotRow);
/* Replace the rows with the sum of that row and a multiple of row i
* so that each new element in column i above row i is zero.*/
rowNb = 0;
for (;rowNb < pivotRow; rowNb++)
{
pTmp = ELEM(pSrc,rowNb,column) ;
pivot = *pTmp;
MAS_ROW_F32(column,pSrc,rowNb,pivot,pSrc,pivotRow);
MAS_ROW_F32(0 ,pDst,rowNb,pivot,pDst,pivotRow);
}
for (rowNb = pivotRow + 1; rowNb < numRows; rowNb++)
{
pTmp = ELEM(pSrc,rowNb,column) ;
pivot = *pTmp;
MAS_ROW_F32(column,pSrc,rowNb,pivot,pSrc,pivotRow);
MAS_ROW_F32(0 ,pDst,rowNb,pivot,pDst,pivotRow);
}
}
/* Set status as ARM_MATH_SUCCESS */
status = ARM_MATH_SUCCESS;
if ((flag != 1U) && (pivot == 0.0f))
{
pIn = pSrc->pData;
for (i = 0; i < numRows * numCols; i++)
{
if (pIn[i] != 0.0f)
break;
}
if (i == numRows * numCols)
status = ARM_MATH_SINGULAR;
}
}
/* Return to application */
return (status);
}
/**
@} end of MatrixInv group
*/
发表于 2022-6-4 02:28:07
楼主
发表于 2022-7-30 18:25:01
