Getting from complex domain to real domain

In summary, "Getting from complex domain to real domain" explores the process of translating intricate theoretical concepts into practical applications. It emphasizes the importance of simplifying complex ideas, identifying key elements, and effectively communicating them to facilitate understanding and implementation in real-world scenarios. The transition involves critical analysis, iterative refinement, and collaboration across disciplines to ensure that solutions are not only theoretically sound but also viable and impactful in practice.
  • #1
jaydnul
558
15
Hi!

I am ok with understanding Euler's formula and how its proven. It is basic mathematic operations that are made possible by the characteristics of i, cos, sin, and exp.

What still makes me uncomfortable is the jump we make at the very beginning or end of calculations, basically Acosx <==> Ae^(jx). The explanations are usually the "real" part of the exponential, and Euler's formula is used to help with this.

But for my complete understanding, taking the real part of something just ins't a "normal" mathematical operation if that makes sense (it is invented for dealing with complex numbers). Is there any other explaination for the transistion we make Acosx <==> Ae^(jx) and why we can do that?
 
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  • #2
jaydnul said:
Hi!

I am ok with understanding Euler's formula and how its proven. It is basic mathematic operations that are made possible by the characteristics of i, cos, sin, and exp.

What still makes me uncomfortable is the jump we make at the very beginning or end of calculations, basically Acosx <==> Ae^(jx). The explanations are usually the "real" part of the exponential, and Euler's formula is used to help with this.

But for my complete understanding, taking the real part of something just ins't a "normal" mathematical operation if that makes sense (it is invented for dealing with complex numbers). Is there any other explaination for the transistion we make Acosx <==> Ae^(jx) and why we can do that?
It is linear algebra. The vectors ##\vec{1}## and ##\vec{\mathrm{i}}## are linear independent over the real numbers. That means that any real expression
$$
\alpha \vec{1} + \beta \vec{\mathrm{i}} = \alpha' \vec{1} +\beta' \vec{\mathrm{i}}
$$
implies
$$
(\alpha-\alpha')\cdot \vec{1} + (\beta-\beta')\cdot \vec{\mathrm{i}}=\vec{0}
$$
and therefore ##\alpha=\alpha' ## and ##\beta=\beta'## by linear independence.
 
  • #3
Another picture of looking at the complex numbers is ##\mathbb{C}=\mathbb{R}[T]/\langle T^2-1 \rangle## which is a quotient ring of the polynomials over the real numbers in one variable ##T.## A complex number is thus a polynomial ##\alpha+\beta\cdot \vec{\mathrm{i}} =\alpha +\beta \cdot T## where we identify ##T^2## with ##-1.## Since ##0 \neq T \neq 1,## we can conclude from ##\alpha+\beta\cdot \vec{\mathrm{i}}=\alpha+\beta\cdot T=0## that ##\alpha = \beta=0.##
 
  • #4
jaydnul said:
Hi!

I am ok with understanding Euler's formula and how its proven. It is basic mathematic operations that are made possible by the characteristics of i, cos, sin, and exp.
Good. That is the hard part.
jaydnul said:
What still makes me uncomfortable is the jump we make at the very beginning or end of calculations, basically Acosx <==> Ae^(jx). The explanations are usually the "real" part of the exponential, and Euler's formula is used to help with this.

But for my complete understanding, taking the real part of something just ins't a "normal" mathematical operation if that makes sense (it is invented for dealing with complex numbers).
It is very normal. If you have a point in two dimensional space, ##(x,y) \in \mathbb{R}## X ##\mathbb{R}## ,it is completely normal to look at its ##x## value. So looking at the real part of ##Ae^{(jx)} = (A\cos(x), A\sin(x))## is normal.
(The question of how and why it was invented is a historical question. It is now standard mathematics, which is all that matters for this discussion.)
 

FAQ: Getting from complex domain to real domain

What is the complex domain?

The complex domain refers to the set of numbers that include both real and imaginary components, typically expressed in the form a + bi, where 'a' and 'b' are real numbers, and 'i' is the imaginary unit defined as the square root of -1. This domain is essential in various fields of mathematics and engineering, particularly in signal processing and control theory.

Why do we need to convert from the complex domain to the real domain?

Converting from the complex domain to the real domain is necessary because many physical systems and measurements are inherently real-valued. For example, in signal processing, while complex numbers can represent signals in a more manageable form (e.g., using phasors), the actual signals that we observe and analyze are real-valued. Thus, we need to extract real components to make sense of the data and apply it to real-world applications.

What are common methods for converting complex numbers to real numbers?

Common methods for converting complex numbers to real numbers include taking the magnitude, phase, or real part of the complex number. The magnitude can be calculated using the formula |z| = √(a² + b²), where z = a + bi. The phase can be determined using the arctangent function, while the real part is simply 'a' in the expression a + bi.

What challenges are associated with this conversion?

One of the main challenges in converting from the complex domain to the real domain is the loss of information. For instance, when taking the magnitude or phase, the original phase information is discarded, which may be crucial for understanding the behavior of certain systems. Additionally, certain operations may introduce non-linearities that complicate the analysis and interpretation of the resulting real values.

How does this conversion apply in practical scenarios, such as in engineering or physics?

In engineering and physics, the conversion from the complex domain to the real domain is commonly applied in areas like electrical engineering (e.g., analyzing AC circuits), control systems, and signal processing. For example, in AC circuit analysis, complex impedance is used to simplify calculations, but the final results, such as voltage and current, must be expressed as real numbers for practical implementation and measurement. This conversion is essential for designing and analyzing systems that operate in the real world.

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