Using CORDIC to perform the QR Factorization System

THE COMPILATION OF COMPLEX MATRICES Using Coordinate Rotation Digital Computer to perform the QR Factorization System
Updated 5 Apr 2024

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  • A good way to write an algorithm intended for a fixed-point target is to write it in MATLAB using built-in floating-point types so we can verify that the algorithm works. When we refine the algorithm to work with fixed-point types, then the best thing to do is to write it so that the same code continues working with floating-point. That way, when we are debugging, then we can switch the inputs back and forth between floating-point and fixed-point types to determine if a difference in behavior is because of fixed-point effects such as overflow and quantization versus an algorithmic difference. Even if the algorithm is not well suited for a floating-point target (as is the case of using CORDIC in the following case), it is still advantageous to have your MATLAB code work with floating-point for debugging purposes. In contrast, we may have a completely different strategy if our target is floating point. For example, the QR algorithm is often done in floating-point with Householder transformations and row or column pivoting. But in fixed-point it is often more efficient to use CORDIC to apply Givens rotations with no pivoting.

Cite As

BLAISE KEVINE (2024). Using CORDIC to perform the QR Factorization System (, MATLAB Central File Exchange. Retrieved .

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