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See: Math Šešeriopai suvoktą daugybą (multiplication) suvokti tensoriais (kovariantiškumu, kontravariantiškumu). ML Baker video lecture about Tensors and tensor products Lines (building up) are easily described by vectors  by construction, generation, spanning. One vector describes a line. We need more vectors to describe a plane, etc. Hyperplanes (tearing down) are easily described in terms of equations, by restriction, by conditions. One equation describes a hyperplane. We need more equations to describe a line, etc. Constructing the most informative illustration of tensors. Use 2x2 change in coordinates. Use coordinate system for equilateral triangles and also a coordinate system for squares. Determine:
Dualities:
Definition of a tensor:
Determinant is topdown to define what is "inside" and what is "outside". A shape like the Moebius band is no fun because you can't make that distinction, you can't "understand" it, it does not make a "marked opposite". It is an unmarked duality. But for understanding we want a primitive marked duality, an irreducible marked duality. This is possible through the six transformations of perspectives given by the Holy Spirit. Negative correlations vs. positive correlations. Yes vs. No. Understanding the Lagrangian. Consider Kinectic Energy as "bottomup" approach and Potential Energy as "topdown" approach. Kinetic Energy is finite and Potential Energy is possibly infinite. DT=−D is (roughly) antiself adjointness.
R is super rich but can't handle itself root wise, algebraically. But just a small "shift" is required to add unmarked opposites and have C. Unmarked opposites are "implications" rather than "explications". Cramer's rule for inverses involves replacing a column in the matrix with the column with the constants. Replacing a column implies a "top down" orthogonal system. Also, the determinant is an antisymmetric topdown system which distinguishes inside and outside. Whereas the symmetric case does not distinguish inside and outside and leaves them as unmarked opposites. In order to have marked opposites, we need to have a system of antisymmetry. Šešeriopai suvoktą dauginimąsi (multiplication) suvokti, išsakyti tensoriais. Thank you very much for your discussion of dimensions. I'm grappling with this very much. But basically I think that you are describing the distinction between the "topdown" view of a space (in which we start with the whole space and break it down) and the "bottomup" view of a space (in which we start with an empty space and build it up). That's what at's the heart of tensors. They combine the two points of view. They break up an n dimensional space into p bottomup (contravariant = vector) and q bottomup (covariant = covector = hyperplane = reflection) points of view. I appreciate your thinking on this difference. I've realized that I need to understand "tensors". They are quite central. They are a generalization of matrices to multiple dimensions. But truly the real point of tensors is that they break space into two different points of view, "top down" and "bottom up", or in other words, covariant and contravariant. https://en.wikipedia.org/wiki/Covariance_and_contravariance_of_vectors Imagine a tetrahedron (in 3 dimensions) with a natural coordinate space on it (given by three edges). That coordinate space, though, has angles that are not 90 degrees. Now consider removing one of these "bottom up" coordinate basis vectors and replacing it with a "top down" vector in the following way: choose a vector that would be perpendicular to the remaining basis vectors. This gives you an entirely different but "dual" basis. I suppose this comes up with the Platonic solids where the "dual" of the cube is the octahedron and vice versa. Well, so tensors describe multidimensional space in terms of two sets of basis vectors. Rather than vectors it turns out it is more natural to think of them as maps (linear functionals) from the vector space to the real numbers. The tensor then "eats" say p many vectors and "yields" say q many vectors. (For a matrix it would be 1 vector in and 1 vector out, making it a linear transformation.) These tensors are very important because, for example, if you want to do integration on a complicated manifold (multidimensional surface) you need that manifold to be oriented, which is to say, have an "inside" and an "outside", or a "left direction" and a "right direction", so that the integration would give opposite sign in either case. So that means you need a wedge product that is antisymmetric, that switches sign whenever you flip your area/volume/etc over, that is, whenver you swap coordinates. Well, all of this to say that I'm realizing that tensors are key to "geometry" because they establish the geometric space between the top down vectors and the bottom up vectors. So I'm thinking that "geometry" is the way of embedding a lower dimensional space into a higher dimensional space. Another insight that's helpful is that any matrix can be decomposed into two matrices, just like "polar decomposition", with length and angle. One matrix is "unitary" and it handles the rotations/reflections but keeps the lengths the same. The other matrix stretches the lengths shorter and longer as needed. https://en.wikipedia.org/wiki/Polar_decomposition Lie groups are typically matrix groups which are also manifolds, so that the actions can be composed in a continuous way, even an infinitesimal way. You can rotate a circle or sphere just the slightest bit. It reminds me of the proof of the Pythagorean theorem given by the truth that "four times a right triangle is the difference of two squares". If the right triangles are long and thin, then one square is just the slightest rotation of the other. So Lie groups are the study of this sort of thing and Lie algebras are their infinitesimal rotations/changes. So circles and spheres and etc. are very central in this subject. Another idea that came up is that in the real numbers the dimensions (of space) are all independent. But what the complexes can be thought of as doing is "coupling" two independent dimensions with a coupling "i". And that i transforms by 90 degrees so that it swaps the y variable with the x variable and keeps track of that. 
TensorNaujausi pakeitimai 
Puslapis paskutinį kartą pakeistas 2016 spalio 15 d., 14:23
