Maximise the following expression subject to the constraint

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The discussion focuses on maximizing the expression P = \underline{w}^H \mathbb{R} \underline{w} under the constraint that \|\underline{w}\| = 1, where \mathbb{R} is defined as \mathbb{G}^H\mathbb{G}. The maximum value of P is achieved by selecting the dominant eigenvector of \mathbb{R}, leading to P = \lambda_{max}. The use of the Cauchy-Schwarz inequality confirms that equality holds when \mathbb{R}\underline{w} = \lambda \underline{w}. The participant seeks validation on the correctness of their approach, particularly regarding the necessity of the eigenvector condition.

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brendan_foo
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Hi guys,

I wish to maximise the following expression subject to the constraint that [itex]\|\underline{w}\| = 1[/itex], and [itex]\mathbb{R}[/itex] is fixed.

[tex] P = \underline{w}^H \mathbb{G}^H\mathbb{G}\underline{w}<br /> = \underline{w}^H \mathbb{R} \underline{w}[/tex]

where

[tex] \mathbb{R} \triangleq \mathbb{G}^H\mathbb{G}[/tex]

I proceed to determine the maximum value, and the value of [itex]\underline{w}[/itex] that achieves it through the general eigenvalue problem and the Cauchy-Schwarz inequality.

Recall that I can only modify w, and its norm is fixed to unity.

[tex] |\langle \underline{w},\mathbb{R}\underline{w}\rangle| \leq \|\underline{w}\|\|\mathbb{R}\underline{w}\|[/tex]

This achieves equality iff

[tex] \mathbb{R}\underline{w} = \lambda \underline{w}[/tex]

and will be subsequently maximised if the dominant eigenvector is chosen, such that

[tex] \mathbb{R}\underline{w} = \lambda_{max} \underline{w}[/tex]

Which then yields a maximum value of [itex]P[/itex] as

[tex] P = \lambda_{max} \|w\|^2 = \lambda_{max}[/tex]

I just want to doubly check with you guys that this is correct.

Thanks!
 
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The if clause is ok, I'm not sure about the only if direction, i.e. whether an eigenvector is forced. The above is a quadratic form, so a classical solution with e.g. Lagrange multipliers should do.
 

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