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Complex Burst Q-less QR Decomposition

Q-less QR decomposition for complex-valued matrices

Since R2020a

  • Complex Burst Q-less QR Decomposition block

Libraries:
Fixed-Point Designer HDL Support / Matrices and Linear Algebra / Matrix Factorizations

Description

The Complex Burst Q-less QR Decomposition block uses QR decomposition to compute the economy size upper-triangular R factor of the QR decomposition A = QR, where A is a complex-valued matrix, without computing Q. The solution to A'Ax = B is x = R\R'\b.

When Regularization parameter is nonzero, the Complex Burst Q-less QR Decomposition block computes the upper-triangular factor R of the economy size QR decomposition of [λInA] where λ is the regularization parameter.

Examples

Ports

Input

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Rows of complex matrix A, specified as a vector. A is a m-by-n matrix where m ≥ 2 and n ≥ 2. If A is a fixed-point data type, A must be signed and use binary-point scaling. Slope-bias representation is not supported for fixed-point data types.

Data Types: single | double | fixed point
Complex Number Support: Yes

Whether inputs are valid, specified as a Boolean scalar. This control signal indicates when the data at the A(i,:) input port is valid. When this value is 1 (true) and the value at ready is 1 (true), the block captures the values at the A(i,:) input port. When this value is 0 (false), the block ignores the input samples.

After sending a true validIn signal, there may be some delay before ready is set to false. To ensure all data is processed, you must wait until ready is set to false before sending another true validIn signal.

Data Types: Boolean

Whether to clear internal states, specified as a Boolean scalar. When this value is 1 (true), the block stops the current calculation and clears all internal states. When this value is 0 (false) and the validIn value is 1 (true), the block begins a new subframe.

Data Types: Boolean

Output

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Rows of the economy size QR decomposition matrix R, returned as a scalar or vector. R is an upper-triangular matrix. The size of the matrix R is min(m,n)-by-n. The output at R(i,:) has the same data type as the input at A(i,:).

Data Types: single | double | fixed point

Whether the output data is valid, specified as a Boolean scalar. This control signal indicates when the data at output port R(i,:) is valid. When this value is 1 (true), the block has successfully computed the matrix R. When this value is 0 (false), the output data is not valid.

Data Types: Boolean

Whether the block is ready, returned as a Boolean scalar. This control signal indicates when the block is ready for new input data. When this value is 1 (true) and the validIn value is 1 (true), the block accepts input data in the next time step. When this value is 0 (false), the block ignores input data in the next time step.

After sending a true validIn signal, there may be some delay before ready is set to false. To ensure all data is processed, you must wait until ready is set to false before sending another true validIn signal.

Data Types: Boolean

Parameters

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Number of rows in input matrix A, specified as a positive integer-valued scalar.

Programmatic Use

Block Parameter: m
Type: character vector
Values: positive integer-valued scalar
Default: 4

Number of columns in input matrix A, specified as a positive integer-valued scalar.

Programmatic Use

Block Parameter: n
Type: character vector
Values: positive integer-valued scalar
Default: 4

Regularization parameter, specified as a nonnegative scalar. Small, positive values of the regularization parameter can improve the conditioning of the problem and reduce the variance of the estimates. While biased, the reduced variance of the estimate often results in a smaller mean squared error when compared to least-squares estimates.

Programmatic Use

Block Parameter: regularizationParameter
Type: character vector
Values: real nonnegative scalar
Default: 0

Tips

Use fixed.getQlessQRDecompositionModel(A) to generate a template model containing a Complex Burst Q-less QR Decomposition block for complex-valued input matrix A.

Algorithms

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Extended Capabilities

Version History

Introduced in R2020a

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