Discussion Overview
The discussion revolves around finding the least square estimator for a matrix B given matrices A and C, where A is expressed as the product of B and C. The scope includes theoretical exploration and mathematical reasoning related to matrix norms and projection operators.
Discussion Character
- Exploratory
- Mathematical reasoning
Main Points Raised
- Bill introduces the problem of estimating matrix B from known matrices A and C, noting that the solution is straightforward for vectors but unclear for matrices.
- One participant questions how the "error" is defined in the context of least squares, suggesting that without a clear definition, the least squares approach lacks specificity.
- Bill proposes using the matrix L2 norm to minimize the sum of squared differences between known matrix pairs A and C, referencing a paper for further context.
- Another participant suggests that if the space of matrices is a Hilbert space, the solution may involve an orthogonal projection of B onto the subspace spanned by A and C.
- Bill acknowledges the existence of a projection operator and mentions the trace in relation to this concept.
- Bill later concludes that the problem can be simplified by breaking matrix B into rows, transforming it into several ordinary least squares problems, each minimizing the squared differences for the respective rows.
Areas of Agreement / Disagreement
The discussion features multiple viewpoints regarding the definition of error and the approach to solving the problem, indicating that no consensus has been reached on a singular method or definition.
Contextual Notes
Participants express uncertainty regarding the definition of error in the least squares context and the implications of using matrix norms and projections in the solution process.