-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathREADME
44 lines (33 loc) · 1.74 KB
/
README
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
Implementation of different optimization algorithms in python using Theano.
Currently it contains:
MINRES
======
MINRES is written completely as a symbolic graph, resulting in longer
compilation time, though possibly faster running time.
The current code is a translation of the matlab code from
http://www.stanford.edu/group/SOL/software.html into python using numpy and
Theano. For a deeper understanding of the algorithm check
http://www.stanford.edu/group/SOL/software.html
Disclaimer: the implementation is meant for Theano users that might want to
fit this algorithm as a building block of some more complicated graph.
While Theano helps when computing the product ``Ax`` is expensive (by
automatically optimizing that expression and translate it into C code or
cuda code) it does make the implementation much less readable than a
pure python/numpy implementation.
MINRES-QLP
==========
MINRES-QLP is written as an op, where all the heavy computation are
done through Theano, while some simple arithmetic and logic are done
in python/numpy.
The current code is a translation of the matlab code from
http://www.stanford.edu/group/SOL/software.html into python using numpy and
Theano. For a deeper understanding of the algorithm check
http://www.stanford.edu/group/SOL/software.html
Disclaimer: the implementation is meant for Theano users that might want to
fit this algorithm as a building block of some more complicated graph.
While Theano helps when computing the product ``Ax`` is expensive (by
automatically optimizing that expression and translate it into C code or
cuda code) it does make the implementation much less readable than a
pure python/numpy implementation.
Contact: Razvan Pascanu (r.pascanu@gmail...)
License: 3-clause BSD