Euclidean metric and non-Cartesian systems

In summary, the conversation discusses the concept of defining Euclidean metric, norm, and scalar product in non-Cartesian systems. It mentions the use of a metric tensor in Riemannian manifolds and how it affects the calculation of distance between points. The conversation also mentions the use of spherical polar coordinates in determining the Euclidean metric. Finally, it asks for recommendations of introductory texts that discuss these issues without relying on the formalism of differential forms.
  • #1
rkaminski
11
0
OK. A problem is simple and probably very stupid... In many (most of) maths books things like Euclidean metric, norm, scalar product etc. are defined in Cartesian coordinate system (that is one which is orthonormal). But what would happen if one were to define all these quantities in non-Cartesian system? For example in many cases (books) the dot product is defined as sum of coordinates for two vectors. Such expression is definitely not true for non-orthogonal systems. Can anyone comment on such issues, perhaps some can propose some detailed reading?
 
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  • #2
Riemannian manifolds have a concept of a metric tensor [itex]g_{ij}[/itex], so that the element of arclength [itex]ds[/itex] is given by [tex]
ds^2 = g_{ij}dx^i dx^j[/tex] in terms of generalized coordinates [itex]x_1, \dots, x_n[/itex], and the distance between two points is then the infimum of [itex]\int_C \frac{ds}{dt}\,dt = \int_C \sqrt{g_{ij}\dot x^i \dot x^j}\,dt[/itex] over all continuous curves [itex]C[/itex] between those two points.

Thus in spherical polar coordinates with [itex]x^1 = r[/itex], [itex]x^2 = \theta[/itex] and [itex]x^3 = \phi[/itex] the euclidean metric is given by [tex]
\begin{array}{ccc}
g_{11} = 1 & g_{12} = 0 & g_{13} = 0 \\
g_{21} = 0 & g_{22} = r^2 & g_{23} = 0 \\
g_{31} = 0 & g_{32} = 0 & g_{33} = r^2 \sin^2 \theta
\end{array}
[/tex] so that the arclength element is given by [tex]
ds^2 = dr^2 + r^2 \sin^2 \theta d\phi^2 + r^2 d\theta^2.
[/tex] Introductory texts on general relativity (such as Foster & Nightingale) should discuss this for the case of the (pseudo-)Riemannian geometry of space-time.
 
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  • #3
And does any author discuss these issues at the elementary level, not pointing out to the formalism of differential forms?
 

FAQ: Euclidean metric and non-Cartesian systems

What is the Euclidean metric?

The Euclidean metric is a mathematical concept used to measure distance in a two or three-dimensional space. It is based on the Pythagorean theorem, where the distance between two points is calculated by taking the square root of the sum of the squares of the differences between their coordinates.

How is the Euclidean metric used in non-Cartesian systems?

The Euclidean metric can be applied to non-Cartesian systems, such as polar coordinates or spherical coordinates, by converting the coordinates to Cartesian coordinates and then using the Euclidean metric formula. This allows for the measurement of distance in curved or non-rectangular spaces.

What are the limitations of the Euclidean metric?

The Euclidean metric assumes that the space being measured is flat and follows the rules of Euclidean geometry. This means that it cannot accurately measure distance in curved or non-Euclidean spaces, such as on a sphere or in a non-Euclidean geometry.

How does the Euclidean metric compare to other distance measurements?

The Euclidean metric is just one way of measuring distance. Other methods include the Manhattan or taxicab metric, which measures distance by adding the absolute differences between coordinates, and the Chebyshev metric, which only considers the largest difference between coordinates. Each metric has its own applications and limitations.

Why is the Euclidean metric important in science?

The Euclidean metric is a fundamental concept in mathematics and is used in many scientific fields, such as physics, engineering, and computer science. It allows for the accurate measurement of distance and is essential in understanding the relationships between objects in space.

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