Prove Polarization Formula in Complex Vector Space

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In summary, the polarization formula is a mathematical formula used to decompose a complex vector into its real and imaginary components. It is derived using the properties of complex numbers and the dot product of vectors. This formula has various applications in quantum mechanics, engineering, and computer science, and it can also be extended to higher dimensions. Its significance lies in its ability to simplify the analysis of complex vectors and solve mathematical problems in various fields.
  • #1
HernanV
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hi, all

Let V be a Complex Vector Space:

probe that:

[tex] <u\mid v> = \frac {1} {4} (\parallel u + v \parallel ^ 2 - \parallel u - v \parallel ^ 2) - \frac {\imath} {4} (\parallel u + \imath v\parallel ^ 2 - \parallel u - \imath v\parallel ^ 2) \forall u,v [/tex]

Polarization formula.

i've multiplied both sides by 4, then aplicated internal product properties and obtained...

[tex] 4 <u\mid v> = 4 <u\mid v> - \imath 4 u [/tex]

please help!

thank you
 
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  • #2
perhaps the minus sign in front of the i/4 should be a plus sign.
 
  • #3
for your question.

To prove the polarization formula in complex vector space, we will start by defining the inner product of two vectors u and v in a complex vector space V as:

<u|v> = u*v + u*v

Where u*v represents the complex conjugate of u multiplied by v. This definition is consistent with the properties of an inner product, such as linearity and conjugate symmetry.

Now, let's consider the left-hand side of the polarization formula:

<u|v> = \frac{1}{4}(\parallel u + v\parallel^2 - \parallel u - v\parallel^2) - \frac{\imath}{4}(\parallel u + \imath v\parallel^2 - \parallel u - \imath v\parallel^2)

Using the definition of the inner product, we can rewrite this as:

<u|v> = \frac{1}{4}((u+v)*(u+v) - (u-v)*(u-v)) - \frac{\imath}{4}((u+\imath v)*(u+\imath v) - (u-\imath v)*(u-\imath v))

Expanding the brackets and simplifying, we get:

<u|v> = \frac{1}{4}(u*u + u*v + v*u + v*v - u*u + u*v - v*u + v*v) - \frac{\imath}{4}(u*u + u*\imath v + \imath u*v + \imath^2 v*v - u*u + u*\imath v - \imath u*v + \imath^2 v*v)

Since \imath^2 = -1, this simplifies to:

<u|v> = \frac{1}{4}(2u*v + 2v*u) - \frac{\imath}{4}(2u*\imath v + 2\imath u*v)

Using the properties of the inner product, we can write this as:

<u|v> = \frac{1}{4}(2(u*v + v*u)) - \frac{\imath}{4}(2\imath(u*v - v*u))

Simplifying further, we get:

<u|v> = \frac{1}{2}(u*v + v*u) - \frac{\imath}{2}(u*v - v*u)

Finally, using the
 

FAQ: Prove Polarization Formula in Complex Vector Space

What is polarization formula in complex vector space?

The polarization formula in complex vector space is a mathematical formula used to decompose a complex vector into its real and imaginary components. It is often used in quantum mechanics to understand the behavior of particles with spin.

How is the polarization formula derived?

The polarization formula is derived using the properties of complex numbers and the dot product of vectors. It involves finding the projection of a vector onto a basis and using this to express the original vector as a sum of the basis vectors.

What is the significance of the polarization formula?

The polarization formula allows us to analyze the properties of complex vectors and understand their behavior in a more simplified manner. It also helps in solving complex mathematical problems involving vectors in quantum mechanics and other fields of science and engineering.

Can the polarization formula be extended to higher dimensions?

Yes, the polarization formula can be extended to higher dimensions. In fact, it is often used in higher dimensions to decompose vectors into their components along different axes. This is useful in analyzing the behavior of higher-dimensional systems.

Are there any applications of the polarization formula?

The polarization formula has various applications in physics, engineering, and computer science. It is used in quantum mechanics, signal processing, image processing, and data compression, to name a few. It is also used in machine learning algorithms to analyze high-dimensional data.

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