Discussion Overview
The discussion revolves around the QR decomposition with a permutation matrix, specifically questioning the correct formulation of the matrix \( R \) in relation to the matrices \( G_i \) and \( P_j \). Participants explore the implications of different definitions and expressions related to QR and LR decompositions.
Discussion Character
- Debate/contested
- Technical explanation
- Mathematical reasoning
Main Points Raised
- Some participants question whether \( R = G_3^{-1}P_1G_2^{-1}P_0G_1^{-1}A \) or \( G_3P_1G_2P_0G_1A = R \) is the correct formulation, noting that it depends on the definitions of \( G_i \) and \( P_j \).
- Others suggest that both expressions are equivalent but not the same, emphasizing the importance of obtaining a right upper matrix through multiplication.
- A participant presents a series of transformations using the Gauss elimination method, leading to a specific matrix \( R \) and questions the correctness of the expressions from earlier posts.
- There is a suggestion that the discussion may actually pertain to LR decomposition rather than QR decomposition, with concerns raised about the orthogonality of the resulting \( Q \).
- Several participants express uncertainty about the correctness of the expression \( R = G_3^{-1}P_1G_2^{-1}P_0G_1^{-1}A \), with some agreeing that it appears incorrect.
Areas of Agreement / Disagreement
Participants do not reach a consensus on the correct expression for \( R \). There are competing views regarding the definitions and implications of the matrices involved, and the discussion remains unresolved regarding the nature of the decomposition (QR vs. LR).
Contextual Notes
Participants note that the resulting matrix \( Q \) is not orthogonal, which raises questions about the validity of the QR decomposition approach being discussed.