| Key | Value |
|---|---|
| FileSize | 2440174 |
| MD5 | 3C52DAAC54F47FEAD8EF2B5926D80831 |
| PackageDescription | Python package for convex optimization CVXOPT is a Python package for convex optimization. It includes * Python classes for storing and manipulating dense and sparse matrices * an interface to most of the double-precision real and complex BLAS * an interface to the dense linear equation solvers and eigenvalue routines from LAPACK * interfaces to the sparse LU and Cholesky solvers from UMFPACK and CHOLMOD. * routines for solving convex optimization problems, an interface to the linear programming solver in GLPK, and interfaces to the linear and quadratic programming solvers in MOSEK * a modeling tool for specifying convex piecewise-linear optimization problems. |
| PackageMaintainer | Ubuntu MOTU Developers <ubuntu-motu@lists.ubuntu.com> |
| PackageName | python-cvxopt |
| PackageSection | python |
| PackageVersion | 1.1-1build1 |
| SHA-1 | E8B6410C70F390C84172A3B76D8549A39DD9F1A2 |
| SHA-256 | 02766FA9A48B892AD046803A300BF302D6C2121AE0956B886F472FC1BF2F5979 |
| hashlookup:children-total | 97 |
| hashlookup:trust | 50 |
The searched file hash includes 97 children files known and seen by metalookup. A sample is included below:
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-cvxopt/examples/doc/chap10/l1svc |
| FileSize | 497 |
| MD5 | 807B22DC8014D1B7E6768D6B62CBF616 |
| SHA-1 | 00BEC9B75D0619A6A99864C48EA703E1AFF37E66 |
| SHA-256 | F4EF025AE46900DE439C2D98A061F15AB13BD46416E48E9243D95D158A78912F |
| SSDEEP | 12:He4AF7NQWQ7HBtu4Zz7lyAKRbogbZKxBbhKgFpKBIWEg:+NF7Ny7S4Zz7lcbogbZKvbhKvMg |
| TLSH | T160F09E0B60627D24EBB7DCD5D18D45997FF500AE1D113E951170070ACE698B05CE3CDE |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-cvxopt/examples/filterdemo/README |
| FileSize | 445 |
| MD5 | 1836C6FE26415939D5A6F32D3E1A1E34 |
| SHA-1 | 020D546E5EF64BE6333D2E45BE1B1DFC32F83C35 |
| SHA-256 | E0BD74AB3AB9E69F55A77A6DEDC7BDE44750C3D250DAFABB2FC61E121AF03095 |
| SSDEEP | 12:eZsDkMJ4/REY7AIKI08goCShQRv26NepL5XFHncqCcoCPPpG:oKkMJ/Y72I0KCQ0v26UpLZFHncjCZG |
| TLSH | T123F02300C40D7DF4E342201BFD321470D8B5C90C23EA30195CFC56E55943DB0D4D1A90 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc-base/cvxopt |
| FileSize | 897 |
| MD5 | CAD6E0ABED96D43BF642EB9273BF8F1F |
| SHA-1 | 02420D85C5115E7CA1D03CC20DA14992EB80654D |
| SHA-256 | 38F54547C2E74F57E87973242DD743D162281EC8F4A96CC808076EB5C83BB79C |
| SSDEEP | 24:6tAyp0aJbWGfxyq8ciKKSuCYqZxpNqDEx6AYqLNm438SsqCSdVlE:QKCbuq8ciKBYKzDxfYaNm4TjxdVlE |
| TLSH | T10911231C916235BC451271CDC78119108F3815A9720B63994C7844B233CB9D9937F3EB |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.6/dist-packages/cvxopt/blas.so |
| FileSize | 206496 |
| MD5 | 322E3342589C2F6A483806B77C919390 |
| SHA-1 | 0363BE0F29A4840A2E679E9BCC4D87B09706B57B |
| SHA-256 | 60448FC80BE10734D9E5E60FDDDFEBF207F59ED13A24F5E59CFA7F49C104945A |
| SSDEEP | 3072:G9bmsiTB+yKBZM7jH/69OTQ9I7aG74dncxuV2jk3:4QY5Bq7aK4dncx5k |
| TLSH | T156149163AB469577C42613B30AEB1364173AE26183F7C736732D09383F92B551B363A9 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.6/dist-packages/cvxopt/glpk.so |
| FileSize | 38160 |
| MD5 | 1FCC157536C02AC01A0BCCCF8599C712 |
| SHA-1 | 0430CDC89BA5ED39522E1A9AD611737756A188C0 |
| SHA-256 | 15FCBDF57BAD99720B4720D371F47E957BF310284995AEE844D61EC9077A24C0 |
| SSDEEP | 768:KHTvDqGYYIF1QFetzqaVtSFcJk8Kwa414:STvMYIF1QFW24tSykiaw |
| TLSH | T17A0384576B43057FC02F13B247C72BA8072AFA4597A7476BB22C81283FA37564B253E5 |
| Key | Value |
|---|---|
| FileName | ./usr/share/pyshared/cvxopt/printing.py |
| FileSize | 5495 |
| MD5 | EC8EDB24392E168A698F0782790DEC56 |
| SHA-1 | 0556AB5D8E5A8B3DACD60646E00DCC84C6B2B470 |
| SHA-256 | D0D873CCA1D6850DFB23DE84C24692A4EEE1A4EB9770FE1BEAA5BFB878E0DCF8 |
| SSDEEP | 96:PvPzR5w1we2/SfGczwtQ10HS9MizYu/UCtgi:X7R5w1weaSfvwtgUSqizYpCai |
| TLSH | T126B12249A5713279C18F056B5CDA404F232BCA97771895303A1D63E90FC36B657B0FB9 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-cvxopt/examples/book/chap4/rls |
| FileSize | 2150 |
| MD5 | 2A30115F6FD41D1A7A42C043BB92AD80 |
| SHA-1 | 09C6C645F20A77C3DA7D67B6DA4F3DC9071B2561 |
| SHA-256 | 53269EBCE14E334B2F6228C18BFFBE13A3603A4EE9F0B97EE7AC203D3AC508E8 |
| SSDEEP | 48:jsgIh5vtaKKgLQmKmkRaaqL8PecqLlZ9GQffQn15eXiYtqf:jsvXv3KgspnvPeHQw615eyYk |
| TLSH | T10641745EF453BFB90D5BC0BED2E93A094B6921BB8D09A912341C2B55C7D3CCC5074A16 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-cvxopt/examples/book/chap6/huber |
| FileSize | 1815 |
| MD5 | 0018CD52B9B1E9D11559CDC8B55DB98E |
| SHA-1 | 0F19013FC870E43C5D00F21930040DFE0E433299 |
| SHA-256 | 759E90D83EE2115618153D69735C1E13478448BA70E7ECBC120F0B03CC2ABB8C |
| SSDEEP | 48:+2s7ngZaTVnWX8LhwO2VFXWxKyS2kDHVYZp8:+2sLnVTL6O7dSDRYZy |
| TLSH | T19531FF28E26BA01E86CBC0B6F4D4FF930E51859DBD06AC56772C9DC4BB0B5558E3C287 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-cvxopt/examples/book/chap6/inputdesign |
| FileSize | 1840 |
| MD5 | 724FDCD7D1DBB7D02DA5E89D8F04A5A0 |
| SHA-1 | 13D7FCB341784540EE1244D9F288D0DC5E586772 |
| SHA-256 | 9B53B64C8857CD85C4838C00A69B4496A140AC25584D5868E57707699DA819F3 |
| SSDEEP | 48:+2Ajn5rJZnKhaQ9GwamkZrm1gG1UQyOgEgeFhgEf:+2Ajn5rJZnMJYVmkZrm1gG1hyOgEgeFD |
| TLSH | T10931EE0A77CE5D56575FE1EDE3E13B05077A826E3E1E2876B13D2D58AC8B8CB4832810 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-cvxopt/examples/doc/chap8/coneqp |
| FileSize | 608 |
| MD5 | 7B609C1E5DFD914CFC8F0FCA3C998A8D |
| SHA-1 | 1841C40EEB0074795CED94E3150BEF8B5EB12C08 |
| SHA-256 | B9706533031DF4EB65175C810C55505DF9E0F0D59A98C4E05FB747E7E48EA2C4 |
| SSDEEP | 12:HYGtjXK/sOf7NQULrVlBE9I81NaJnjkar4nkMIal3nV7:4GtjnC7NfPB+I8ijkdkMlF7 |
| TLSH | T111F0284BC006B828E6B4E02A885A2C920E75DD086D2BB0003D3E55A08FAA2A2CD35756 |