LBFGS and OWL-QN optimization algorithms
Project description
PyLBFGS
This is a Python wrapper around Naoaki Okazaki (chokkan)’s liblbfgs library of quasi-Newton optimization routines (limited memory BFGS and OWL-QN).
This package aims to provide a cleaner interface to the LBFGS algorithm than is currently available in SciPy, and to provide the OWL-QN algorithm to Python users.
Installing
Type:
pip install pylbfgs
Hacking
Type:
pip install -r requirements.txt cython lbfgs/_lowlevel.pyx python setup.py build_ext -i
to build PyLBFGS in-place, i.e. without installing it.
To run the test suite, make sure you have Nose installed, and type:
nosetests tests/
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
PyLBFGS-0.2.0.3.tar.gz
(90.3 kB
view hashes)
Built Distributions
Close
Hashes for PyLBFGS-0.2.0.3-py2.7-linux-x86_64.egg
Algorithm | Hash digest | |
---|---|---|
SHA256 | 64504344455d32a90757e0bff60a3d034fa2f472b3d8491614c43118323a3dec |
|
MD5 | ea8fc2f64592c47f75d1beb50f81b005 |
|
BLAKE2b-256 | 408aadea05a41e5c442a205947e3cd32cb5b9c71ed8b159d7c3c9fed68fa1f14 |
Close
Hashes for PyLBFGS-0.2.0.3-cp36-cp36m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2299c0769ffdb955249060a505975c8928b9d8204de65d9818dbf22367866bf0 |
|
MD5 | 98585d642c7caf0b51756357edb3ac1a |
|
BLAKE2b-256 | 6c442106a9c694ba62f244ed30d71120af43336f46db39e95a1702d50afcfc2f |
Close
Hashes for PyLBFGS-0.2.0.3-cp36-cp36m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2f7ec09edd29fbf819dcb43142af06bfe945bb22d761ccfefd55be5eb0af4089 |
|
MD5 | f8c812112895afbc3f93ec4692a658af |
|
BLAKE2b-256 | f317bb8fa3bed0e562893150d2f3ca5e7bfede90b6f0751d22529948a637a29c |
Close
Hashes for PyLBFGS-0.2.0.3-cp36-cp36m-macosx_10_11_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6e32c0da7817e4f30603bcb26e3aa5ad0bcc7a5223ee3ea399544c9890fe1400 |
|
MD5 | 78929aea0692a40f30704a38a771d702 |
|
BLAKE2b-256 | 359a1b0b4c441030baf6525ee461717cabd1e8b94e75100569f4b83c389407f5 |
Close
Hashes for PyLBFGS-0.2.0.3-cp35-cp35m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e501fdd3f594f10028fb64fc6b06c756967cc211b2c76ca9dfd7ecb7ea3b0167 |
|
MD5 | eee72c8c37baef7df1a473e9a69e5141 |
|
BLAKE2b-256 | f5ac5ec7a791331a6bbaccedbede3ce821d8f2a248b3858f1ceb603b3c76ddd0 |
Close
Hashes for PyLBFGS-0.2.0.3-cp35-cp35m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 91f3f0fa6994ffe674057d3899073518b69c5097dbc2110190b94afd01247fc9 |
|
MD5 | af203cccf73061c750962ba3016da59b |
|
BLAKE2b-256 | 4e4617f6aea7cfd6ffd9e2ee4ed80d51b45d46fc9647f12be954adc3fd7de82f |
Close
Hashes for PyLBFGS-0.2.0.3-cp35-cp35m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c1c851ec8c10191928e071a5bcd4ed3e5b7e37dda405a294645ceb42ba856c7b |
|
MD5 | 633f0a73e370be912617cc44e7f80273 |
|
BLAKE2b-256 | fe6d4701027e1bc1a8d974628edd9ae943b9ef186f73a1e2080cb0a71a78a14b |
Close
Hashes for PyLBFGS-0.2.0.3-cp34-cp34m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f07256eb429a93868fc1b9f7e1974d133661ab9b09a8ee2379e7a46df97c7eac |
|
MD5 | e89948c759c079effee051854154d3d8 |
|
BLAKE2b-256 | edd37781657b97ca9c0d9f24f5937cf69088bfba40938a6a2f095428f69b63c8 |
Close
Hashes for PyLBFGS-0.2.0.3-cp34-cp34m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 85f4004f54aab74fad1a0ad0114919e4b1c38ba4e08391dfc0d4ac76ead1b81c |
|
MD5 | 88bfe18c19d48f30456fbf22444488cf |
|
BLAKE2b-256 | f991670aa98da57b7a00a863914b0dd9132288662961c32256ba2d5b58226cb9 |
Close
Hashes for PyLBFGS-0.2.0.3-cp34-cp34m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 57f3230b64aa6042b605d3b4077d22699c8e5541e8b5c30c4a78a751a3827273 |
|
MD5 | 2623d832512fe138499d8d502849096e |
|
BLAKE2b-256 | 6dad97e78b1c1a8dc5d74b3e6ec06def4a4ea0ad1d8c8ec42735fc54585c486a |
Close
Hashes for PyLBFGS-0.2.0.3-cp34-cp34m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 10980623cad00cbe6c9dbabb22f3867684a1fb8c11b64618bfd9d0afdb110b91 |
|
MD5 | 38ee677ca2017f9e21e9bf3bb4f0abe4 |
|
BLAKE2b-256 | 183804fca6557ecac21a235e971f41e7dbd851a650f275a1e4fb9b40899a5d55 |
Close
Hashes for PyLBFGS-0.2.0.3-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 33a7384a68667fd0b33563e539cc2b3385670fd0afc08a273ff79d848cb91c1b |
|
MD5 | 4ebb7796fed768b514d0c79406de1bfe |
|
BLAKE2b-256 | 823a1f6eacc68ad1125efbdcd5dca004cf6ef3bbd3cafc28bb98e2d6b68a2bb0 |
Close
Hashes for PyLBFGS-0.2.0.3-cp27-cp27mu-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 514e3cf6f90448a70555a1c4c2148b2eca084513a35de49356fe8463165937e4 |
|
MD5 | 0b131a889ee2fc5a450fcf306812590d |
|
BLAKE2b-256 | 3f28ee312d5755b3847f9743bfcc51e9d2ed64130abc93a4023e8e16c49c09bc |
Close
Hashes for PyLBFGS-0.2.0.3-cp27-cp27m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b34ec2da81c056bc5027cd13692e95799105cc3b32d951989c3d36970dec0ea3 |
|
MD5 | ccc346a7d8696685858b2d4264125ec5 |
|
BLAKE2b-256 | 6e474ad7692e7cac0ac8f3e16ce0c04fa208a1e6b477869ce1cd1222b2075f37 |
Close
Hashes for PyLBFGS-0.2.0.3-cp27-cp27m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 08207578f70af39e9aa00b744fd8aac62a2334a29e1e8c3f51a2072891b7509b |
|
MD5 | 1ed89ca41d498835156dfce93f5171fd |
|
BLAKE2b-256 | ed3ce04d801b8bdd3b20b95aad0e34f5e2a8c87e334ac38920b7874aee145a0d |
Close
Hashes for PyLBFGS-0.2.0.3-cp27-cp27m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a66fafba37fad1b91db651df48c0f146dc3489c5832cd69c81435c55c747a923 |
|
MD5 | 1deba1d2d9218e44dde5d26e8ac6ccf0 |
|
BLAKE2b-256 | 4ba25aab63975e0cf43e7541cfbc636abf6448965d07b3fc84d2391185da3629 |
Close
Hashes for PyLBFGS-0.2.0.3-cp27-cp27m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2189eab4ce7f83162b760ce847f84585482e4f907fbbec3a4ee18991caa17a23 |
|
MD5 | cfb3c43be18acd99f0183bedc4168943 |
|
BLAKE2b-256 | e9e0ea444918b65bb8a1d80e73add89c840e4f8e689dd6b5dd201fac4f819a94 |
Close
Hashes for PyLBFGS-0.2.0.3-cp27-cp27m-macosx_10_11_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | bfff8a074f2fb511b5df13cbe480da5be0dc4d392d836a8d96dbde46e3e5ce16 |
|
MD5 | 6542704a358649a6ad9b1ff309c36063 |
|
BLAKE2b-256 | 8f0316d85a0401c513d67f3747973bb8179ac6f35a107f1f654d08ba3d5108ae |
Close
Hashes for PyLBFGS-0.2.0.3-cp27-cp27m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 368a6a5dcb7a3d8ebf9616355d939327491f1bf01dcc2296046ee8b3534451b9 |
|
MD5 | 121a51b686eeb0b8a85c2b25139b375f |
|
BLAKE2b-256 | d4c837eed77a4e12b3cf94174c1e40a376934d8b5366d0cc442f0b59dbcc9e78 |