Multi-objective recursive least squares

In summary, the conversation discusses the possibility of using Woodbury's matrix identity for a recursive update in a multiobjective least squares solution. The speaker mentions a generalization of Woodbury's identity that can work for a rank m update, but notes that it isn't surprising that inverting a rank m matrix is necessary. They also mention that the generalization is usually referred to as the matrix inversion lemma.
  • #1
Superfish
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0
Is this possible?

I've computed a multiobjective least squares solution and want to make it able to be updated recursively but I get stuck at applying the woodbury matrix identity since it's no longer a rank 1 udpate. Are there any derivations of this anywhere or is this not possible? Thanks
 
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  • #2
Of course, if you have a rank 2 update, you can always use Woodbury's twice in a row, etc.

A generalization of Woodbury's identity is
[tex]
(A+BCD)^{-1}=A^{-1} - A^{-1} B(D A^{-1} B + C^{-1} )^{-1} D A^{-1}
[/tex]
Where A is nxn, B is nxm, C is mxm, and D is mxn, so this can work for a rank m update. It isn't too suprising that you have to invert a rank m matrix to do it.


jason

EDIT: just realized that Woodbury's was never stated. In the above, if B is nx1 vector, D is the transpose of B and C is just the number 1, then you have Woodbury's identity. The generalization above is usually called the matrix inversion lemma
 
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Related to Multi-objective recursive least squares

1. What is Multi-objective recursive least squares (MORLS)?

Multi-objective recursive least squares is a mathematical method used in regression analysis to estimate the parameters of a linear model with multiple objectives or criteria. It is an extension of the traditional recursive least squares method, which only considers a single objective.

2. How is MORLS different from traditional recursive least squares?

MORLS differs from traditional recursive least squares in that it takes into account multiple objectives in the estimation process. This allows for a more comprehensive analysis of the data and can lead to more accurate parameter estimates.

3. What are some applications of MORLS?

MORLS is commonly used in fields such as economics, engineering, and social sciences to analyze data with multiple objectives. It has also been applied in areas such as signal processing, system identification, and control systems.

4. What are the advantages of using MORLS?

One of the main advantages of MORLS is its ability to handle multiple objectives, which can provide a more complete understanding of the data. Additionally, MORLS is a computationally efficient method and can handle large datasets.

5. Are there any limitations to using MORLS?

One limitation of MORLS is that it assumes linearity in the relationship between the independent and dependent variables. It may not perform well in cases where the relationship is non-linear. Additionally, MORLS may produce biased estimates if the data is not normally distributed.

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