### Riemannian Geometry on the Computer

Pick a Riemannian manifold, perhaps a Minkowsy manifold (with signature <− + + +>). Pick a general point on the manifold and then a coordinate system for which the metric tensor is ηij at the chosen point. (ηij = δij except for η00 which is −1.) In a neighborhood of the point expand the metric tensor in a taylor series, keeping two or three terms. Each element of this tensor is a Multi Variate Polynomial (mvp). We must compute the matrix inverse of gij. Even for matrices we have
(1 − x)−1 = Σ{j = 0, ...} xj. We want (η − x)−1 in a similar form. Note that x and η don’t commute.
(η − x)−1 = (1 − η−1x)−1η−1 = Σ{j = 0, ...} (η−1x)jη−1
In the case at hand η−1 = η and so we get:
(η − x)−1 = Σ{j = 0, ...} (ηx)jη.
Terms beyond the third vanish, because we retain only the terms of degree less than 4. There is no question of convergence. This means that we can compute the contravariant gij exactly without even a divide! I find this bizarre! The Scheme routine inv performs the above inversion.

I will use the following definition of the Riemann curvature tensor:
Rμναβ = ∂αΓμνβ − ∂βΓμνα + ΓμσαΓσνβ − ΓμσβΓσνα

### Behavior of some tensor code

g1, g2, g3, g4
gn takes a procedure of n arguments and returns a procedure of n arguments that stores dimn values.
if 0 <= i, j, k, l < dim and the free variables of zot are among i, j, k, l then ((g4 (lambda (i j k l) zot)) i j k l) yields zot. (gN fun) is subsequently better than fun merely for the economy of not recomputing the values.

#### Operators for multivariate polynomials

((padd n k) p1 p2) adds two polynomials, p1 & p2, in n variables. The inputs must be of at least degree k−1 and the yield will be of degree k−1. ((pmul n k) p1 p2) multiplies polynomials discarding terms of order higher than k−1. (pgen n k ng z) generates a polynomial in n variables of degree k−1. It uses the yield of the 0 argument procedure ng for an element, except for the constant term, which is z.

Tentative embodiment: A degree n polynomial is a vector of the n+1 coefficients where element 0 is the constant coefficient. A polynomial in k variables has coefficients which are polynomials in k−1 variables. By the “degree” of a multivariate polynomial, we mean the maximum, over the terms, of the sums of the exponents of the variables.

#### Example

(pgen 3 5 (let ((x 0))(lambda()(set! x (+ x 1)) x)) −9) yields
#(#(#(−9 34 33 32 31) #(27 30 29 28) #(24 26 25) #(22 23) #(21))
#(#(11 20 19 18) #(15 17 16) #(13 14) #(12)) #(#(5 10 9) #(7 8) #(6))
#(#(2 4) #(3)) #(#(1)))

See the code.

((iter n)(lambda (k) (... k ...))) performs (... k ...) for k descending from n−1 thru 0.
(ip e a b) returns the inner product of two 1D mvps; e terms.
(mm a b) is the matrix product of 2 2D matrices.
(ftl g) turns a 1D vector into a list.
(curry g) curries the function g. If f = (curry g) then ((f x) y z) = (g x y z).
((ml n) g) returns an n deep list of values from a n-ary function g.

Alas the program reports that Bianchi’s identity is false. I have not had time to find the bug.