Real Burst Matrix Solve Using QR Decomposition

Compute the value of x in the equation Ax = B for real-valued matrices using QR decomposition

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  • Library:

  • Fixed-Point Designer / Matrices and Linear Algebra / Linear System Solvers

  • Real Burst Matrix Solve Using QR Decomposition block

Description

The Real Burst Matrix Solve Using QR Decomposition block solves the system of linear equations Ax = B using QR decomposition, where A and B are real-valued matrices. To compute x = A-1, set B to be the identity matrix.

Creation

fixed.getMatrixSolveModel(A,B) generates a template model containing a Real Burst Matrix Solve Using QR Decomposition block for real-valued input matrices A and B.

Ports

Input

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Rows of real matrix A, specified as a vector. A is an m-by-n matrix where m ≥ 2 and m ≥ n. If B is single or double, A must be the same data type as B. If A is a fixed-point data type, A must be signed, use binary-point scaling, and have the same word length as B. Slope-bias representation is not supported for fixed-point data types.

Data Types: single | double | fixed point

Rows of real matrix B, specified as a vector. B is an m-by-p matrix where m ≥ 2. If A is single or double, B must be the same data type as A. If B is a fixed-point data type, B must be signed, use binary-point scaling, and have the same word length as A. 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,:) and B(i,:) input ports are valid. When this value is 1 (true) and the value at ready is 1 (true), the block captures the values on the A(i,:) and B(i,:) input ports. When this value is 0 (false), the block ignores the input samples.

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 matrix X, returned as a scalar or vector.

Data Types: single | double | fixed point

Whether the output data is valid, returned as a Boolean scalar. This control signal indicates when the data at the output port X(i,:) is valid. When this value is 1 (true), the block has successfully computed a row of matrix X. 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.

Data Types: Boolean

Parameters

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Number of rows in input matrices A and B, 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

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

Programmatic Use

Block Parameter: p
Type: character vector
Values: positive integer-valued scalar
Default: 1

Data type of the output matrix X, specified as fixdt(1,18,14), double, single, fixdt(1,16,0), or as a user-specified data type expression. The type can be specified directly, or expressed as a data type object such as Simulink.NumericType.

Programmatic Use

Block Parameter: OutputType
Type: character vector
Values: 'fixdt(1,18,14)' | 'double' | 'single' | 'fixdt(1,16,0)' | '<data type expression>'
Default: 'fixdt(1,18,14)'

Model Examples

Implement Hardware-Efficient Complex Burst QR Decomposition

Implement Hardware-Efficient Complex Burst QR Decomposition

Demonstrates how to compute the QR decomposition of complex matrices using hardware-efficient MATLAB® code in Simulink®. This model shares computational resources across steps of the QR Decomposition algorithm. It thus uses fewer on chip resources than a fully pipelined approach, while sacrificing some total throughput.

Implement Hardware-Efficient Real Burst Matrix Solve

Implement Hardware-Efficient Real Burst Matrix Solve

Implement Hardware-Efficient Real Burst Matrix Solve.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

See Also

Blocks

Functions

Introduced in R2019b

Fixed-Point Designer Documentation

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