2025-06-08

1153: For Euclidean Vectors Space with Euclidean Inner Product, Nonzero Vector, and Real Positive-Definite Symmetric Matrix, Vector That Maximizes Its Inner Product with Nonzero Vector with Condition That Its Bilinear Form by Matrix Is 1 Is This

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description/proof of that for Euclidean vectors space with Euclidean inner product, nonzero vector, and real positive-definite symmetric matrix, vector that maximizes its inner product with nonzero vector with condition that its bilinear form by matrix is 1 is this

Topics


About: vectors space

The table of contents of this article


Starting Context



Target Context


  • The reader will have a description and a proof of the proposition that for any Euclidean vectors space with the Euclidean inner product, any nonzero vector, and any real positive-definite symmetric matrix, the vector that maximizes its inner product with the nonzero vector with the condition that its bilinear form by the matrix is 1 is this.

Orientation


There is a list of definitions discussed so far in this site.

There is a list of propositions discussed so far in this site.


Main Body


1: Structured Description


Here is the rules of Structured Description.

Entities:
\(\mathbb{R}^n\): \(= \text{ the Euclidean vectors space }\) with the Euclidean inner product, \(\langle \bullet, \bullet \rangle\)
\(v_0\): \(\in \mathbb{R}^n\) such that \(v \neq 0\)
\(M\): \(\in \{\text{ the } n \times n \text{ real positive-definite symmetric matrices }\}\)
\(f\): \(: \mathbb{R}^n \to \mathbb{R}, v \mapsto \langle v, v_0 \rangle\)
\(g\): \(: \mathbb{R}^n \to \mathbb{R}, v \mapsto \langle M v, v \rangle\)
//

Statements:
\(v \in \mathbb{R}^n\) that maximizes \(f (v)\) with the condition that \(g (v) = 1\) is \(v = 1 / \sqrt{{v_0}^t M^{-1} v_0} M^{-1} v_0\)
//


2: Proof


Whole Strategy: Step 1: see that \(f (v) = {v_0}^t v\) and \(g (v) = v^t M v\); Step 2: find an orthogonal matrix multiplied by a positive diagonal matrix from the right, \(T^{-1}\), such that \({T^{-1}}^t M T^{-1} = I\); Step 3: see that \(f (v) = {v_0}^t T^{-1} T v\) and \(g (v) = v^t T^t {T^{-1}}^t M T^{-1} T v = (T v)^t I T v\); Step 4: take \(T v = c {T^{-1}}^t v_0\) and compute \(c\).

Step 1:

By the Euclidean inner product, \(f (v) = \langle v, v_0 \rangle = {v_0}^t v\) and \(g (v) = \langle M v, v \rangle = v^t M v\).

Step 2:

If \(M = I\), obviously, \(v\) will be a scalar multiple of \(v_0\), but that is not necessarily the case.

So, our strategy is to find an orthogonal matrix multiplied by a positive diagonal matrix from the right, \(T^{-1} = U D\), such that \({T^{-1}}^t M T^{-1} = I\), which is possible by the proposition that any positive-definite Hermitian matrix can be transformed to the identity by a unitary matrix multiplied by a positive diagonal matrix from right: we take it to be \(T^{-1}\) instead of \(T\) just for convenience of expressions, which is valid because \(U D\) is invertible.

Step 3:

\(f (v) = {v_0}^t T^{-1} T v\). \({v_0}^t T^{-1} \neq 0^t\), because otherwise, \({v_0}^t = {v_0}^t T^{-1} T = 0^t T = 0^t\), a contradiction.

\(g (v) = v^t {{T^{-1}}^t}^{-1} {T^{-1}}^t M T^{-1} T v = v^t T^t {T^{-1}}^t M T^{-1} T v\), by the proposition that for any commutative ring, the inverse of the transpose of any invertible matrix is the transpose of the inverse of the matrix because \({{T^{-1}}^t}^{-1} = {{T^{-1}}^{-1}}^t = T^t\), \(= v^t T^t I T v = (T v)^t I T v\), by the proposition that for any commutative ring, the transpose of the product of any matrices is the product of the transposes of the constituents in the reverse order.

Step 4:

So, the problem has become of finding the \(T v\) maximizing \({v_0}^t T^{-1} T v\) with the condition that \((T v)^t I T v = 1\).

Obviously, \(T v = c ({v_0}^t T^{-1})^t = c {T^{-1}}^t v_0\) for a \(c \in \mathbb{R}\).

So, \(v = T^{-1} T v = T^{-1} c {T^{-1}}^t v_0 = c T^{-1} {T^{-1}}^t v_0\).

From \({T^{-1}}^t M T^{-1} = I\), \(M T^{-1} = {{T^{-1}}^t}^{-1} {T^{-1}}^t M T^{-1} = {{T^{-1}}^t}^{-1} I = {{T^{-1}}^t}^{-1}\), \(T^{-1} = M^{-1} M T^{-1} = M^{-1} {{T^{-1}}^t}^{-1}\), and \(T^{-1} {T^{-1}}^t = M^{-1} {{T^{-1}}^t}^{-1} {T^{-1}}^t = M^{-1} I = M^{-1}\).

So, \(v = c M^{-1} v_0\).

\(v^t M v = (c M^{-1} v_0)^t M c M^{-1} v_0 = c {v_0}^t {M^{-1}}^t M c M^{-1} v_0 = c^2 {v_0}^t {M^{-1}}^t v_0 = 1\), so, \(c = 1 / \sqrt{{v_0}^t {M^{-1}}^t v_0}\): it needs to be positive, because \({v_0}^t v = {v_0}^t c M^{-1} v_0 = c {v_0}^t M^{-1} v_0 = c {v_0}^t {M^t}^{-1} M^t M^{-1} v_0 = c {v_0}^t {M^{-1}}^t M M^{-1} v_0 = c (M^{-1} v_0)^t M M^{-1} v_0\) where \(0 \le (M^{-1} v_0)^t M M^{-1} v_0\) because \(M\) is positive-definite.

As \(M\) is symmetric, \({M^{-1}}^t = {M^t}^{-1} = M^{-1}\).

So, \(v = 1 / \sqrt{{v_0}^t M^{-1} v_0} M^{-1} v_0\).


References


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