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Complex Burst Q-less QR Decomposition with Forgetting Factor Whole R Output

Q-less QR decomposition for complex-valued matrices with infinite number of rows

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

  • Screenshot of Complex Burst Q-less QR Decomposition with Forgetting Factor Whole R Output

Description

The Complex Burst Q-less QR Decomposition with Forgetting Factor Whole R Output block uses QR decomposition to compute the economy size upper-triangular R factor of the QR decomposition, A = QR, without computing Q. A is an infinitely tall complex-valued matrix representing streaming data.

When the regularization parameter is nonzero, the Complex Burst Q-less QR Decomposition with Forgetting Factor Whole R Output block initializes the first upper-triangular factor R to λIn before factoring in the rows of A, where λ is the regularization parameter and In = eye(n)

Ports

Input

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Rows of complex matrix A, specified as a vector. A is an infinitely tall matrix of streaming data. If A uses 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

Whether inputs are valid, specified as a Boolean scalar. This control signal indicates when the data from the A(i,:) input port is valid. When this value is 1 (true) and the value of 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 value at validIn is 1 (true), the block begins a new subframe.

Data Types: Boolean

Output

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Economy size QR decomposition matrix R, returned as a vector. R is an upper triangular matrix. The size of matrix R is n-by-n. R has the same data type as A.

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 validIn 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 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

Forgetting factor applied after each row of the matrix is factored, specified as a real positive scalar. The output is updated as each row of A is input indefinitely.

Programmatic Use

Block Parameter: forgetting_factor
Type: character vector
Values: real positive scalar
Default: 0

Regularization parameter, specified as a real 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

Algorithms

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Extended Capabilities

Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.

Version History

Introduced in R2022b