numpy.linalg.multi_dot(arrays)
[source]
Compute the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order.
multi_dot
chains numpy.dot
and uses optimal parenthesization of the matrices [1] [2]. Depending on the shapes of the matrices, this can speed up the multiplication a lot.
If the first argument is 1D it is treated as a row vector. If the last argument is 1D it is treated as a column vector. The other arguments must be 2D.
Think of multi_dot
as:
def multi_dot(arrays): return functools.reduce(np.dot, arrays)
Parameters: 


Returns: 

See also
dot
The cost for a matrix multiplication can be calculated with the following function:
def cost(A, B): return A.shape[0] * A.shape[1] * B.shape[1]
Assume we have three matrices .
The costs for the two different parenthesizations are as follows:
cost((AB)C) = 10*100*5 + 10*5*50 = 5000 + 2500 = 7500 cost(A(BC)) = 10*100*50 + 100*5*50 = 50000 + 25000 = 75000
[1]  Cormen, “Introduction to Algorithms”, Chapter 15.2, p. 370378 
[2]  https://en.wikipedia.org/wiki/Matrix_chain_multiplication 
multi_dot
allows you to write:
>>> from numpy.linalg import multi_dot >>> # Prepare some data >>> A = np.random.random((10000, 100)) >>> B = np.random.random((100, 1000)) >>> C = np.random.random((1000, 5)) >>> D = np.random.random((5, 333)) >>> # the actual dot multiplication >>> _ = multi_dot([A, B, C, D])
instead of:
>>> _ = np.dot(np.dot(np.dot(A, B), C), D) >>> # or >>> _ = A.dot(B).dot(C).dot(D)
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.linalg.multi_dot.html