This example shows how to implement a hardware-efficient least-squares solution to the real-valued matrix equation
using the Real Partial-Systolic Matrix Solve Using QR Decomposition block. This method is known as diagonal loading, and is known as a regularization parameter.
When you set the regularization parameter to a non-zero value in block Real Partial-Systolic Matrix Solve Using QR Decomposition, then the block computes the the least-squares solution to
where is an n-by-n identity matrix and is an array of zeros of size n-by-p.
This method is known as diagonal loading, and is known as a regularization parameter. The familiar textbook least-squares solution for this equation is the following, which is derived by multiplying both sides of the equation by and taking the inverse of the matrix on the left-hand side.
Real Partial-Systolic Matrix Solve Using QR Decomposition block computes the solution efficiently without computing an inverse by computing the QR decomposition, transforming
in-place to upper-triangular R, and transforming
and solving the transformed equation .
Specify the number of rows in matrices A and B, the number of columns in matrix A, and the number of columns in matrix B.
m = 300; % Number of rows in matrices A and B n = 10; % Number of columns in matrix A p = 1; % Number of columns in matrix B
When the regularization parameter is non-zero in block Real Partial-Systolic Matrix Solve Using QR Decomposition, then the diagonal-loading method is used. When the regularization parameter is zero, then the equations reduce to the regular least-squares formula AX=B.
regularizationParameter = 1e-3;
For this example, use the helper function
realRandomLeastSquaresMatrices to generate random matrices A and B for the least-squares problem AX=B. The matrices are generated such that the elements of A and B are between -1 and +1, and A has rank r.
rng('default') r = 3; % Rank of matrix A [A,B] = fixed.example.realRandomLeastSquaresMatrices(m,n,p,r);
Use the helper function
realQRMatrixSolveFixedpointTypes to select fixed-point data types for input matrices A and B, and output X such that there is a low probability of overflow during the computation.
max_abs_A = 1; % Upper bound on max(abs(A(:)) max_abs_B = 1; % Upper bound on max(abs(B(:)) precisionBits = 24; % Number of bits of precision T = fixed.realQRMatrixSolveFixedpointTypes(m,n,max_abs_A,max_abs_B,precisionBits); A = cast(A,'like',T.A); B = cast(B,'like',T.B); OutputType = fixed.extractNumericType(T.X);
model = 'RealPartialSystolicQRDiagonalLoadingMatrixSolveModel'; open_system(model);
The Data Handler subsystem in this model takes real matrices A and B as inputs. The
ready port triggers the Data Handler. After sending a true
validIn signal, there may be some delay before
ready is set to false. When the Data Handler detects the leading edge of the
ready signal, the block sets
validIn to true and sends the next row of A and B. This protocol allows data to be sent whenever a leading edge of the
ready signal is detected, ensuring that all data is processed.
Use the helper function
setModelWorkspace to add the variables defined above to the model workspace. These variables correspond to the block parameters for the Real Partial-Systolic Matrix Solve Using QR Decomposition block.
numSamples = 1; % Number of sample matrices fixed.example.setModelWorkspace(model,'A',A,'B',B,'m',m,'n',n,'p',p,... 'regularizationParameter',regularizationParameter,... 'numSamples',numSamples,'OutputType',OutputType);
out = sim(model);
The Real Partial-Systolic Matrix Solve Using QR Decomposition block outputs data one row at a time. When a result row is output, the block sets
validOut to true. The rows of X are output in the order they are computed, last row first, so you must reconstruct the data to interpret the results. To reconstruct the matrix X from the output data, use the helper function
X = fixed.example.matrixSolveModelOutputToArray(out.X,n,p,numSamples);
To evaluate the accuracy of the Real Partial-Systolic Matrix Solve Using QR Decomposition block, compute the relative error.
A_lambda = [regularizationParameter * eye(n);A]; B_0 = [zeros(n,p);B]; relative_error = norm(double(A_lambda*X - B_0))/norm(double(B_0)) %#ok<NOPTS>
relative_error = 1.2189e-04