This small library optimizes the associative order of matrix multiplication (in the future -- tensor contraction) using a dynamic programming algorithm.

Consider 3 matrices A_(50 . 5), B_(5 . 100), C_(100 . 10). We can obtain matrix

D = A * B * C;

in two ways:

Using the first way gives 50*5*100 + 50*100*10 = 75000 scalar multiplications. Using the second way gives 5*100*10 + 50*5*10 = 7500, which is ten times less. Our goal is to minimize the number of multiplications.

Function %build-tree takes a vector of dimensions, #((4 . 5) (5 . 10) (10 . 2) (2 . 8) (8 . 6) (6 . 5) (5 . 10) ...) (every element corresponds to (height . width) of a matrix) and returns list of (the best tree for the whole range from 0 to n, the resulting matrix size, the total number of scalar multiplications)

Dynamic programming - we solve it for each subrange (from i to j).

Ranges of length 1 and 2 are trivial. For range from i to (+ i k) now -- there are k-1 options, of which we chose the best one. Options are:

• multiply i matrix by the best of range (+ i 1) to (+ i k)

• multiply the best of range i, (+ i 1) to the best of range (+ i 2) (+ i k)

• multiply the best of range i, (+ i 2) to the best of range (+ i 3) (+ i k)

...etc

The best one is the one with the lowest (total) amount of multiplications.

MIT. Contributions are welcome.