The problem I am thinking we might try to might fix is that programmers
with less numerical competence is unaware that the matrix expression
(X**-1) * Y
should be written as
np.linalg.solve(X,Y)
I've seen numerous times matrix expressions being typed exactly as
written in linear
On 7/17/2011 1:57 PM, Sturla Molden wrote:
I suggest inverting a NumPy matrix could result in an unsolved linear
system type, instead of actually computing the matrix inverse and
returning a new matrix.
1. Too implicit.
2. Too confusing for new users.
2a. Too confusing for students.
Something related: This autumn I expect to invest a significant amount of time
(more than four weeks full-time) in a package for lazily evaluated, polymorphic
linear algebra.
Matrix = linear operator, a type seperate from arrays -- arrays are treated as
vectors/stacked vectors
Matrices can be
More concrete feedback about Sturla's proposal: The problem I have is if you do
A = B**-1
Then, A is some 'magic' object, not a NumPy array. That means that it is very
different from Matlab's \, which restricts the context, you simply can't do
A = B \
I think
A.solve(u)
is a lot clearer in
