| Key | Value |
|---|---|
| FileSize | 1345992 |
| MD5 | 08B21F50FAE6266BE188326F597CE544 |
| 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 Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
| PackageName | python-cvxopt |
| PackageSection | python |
| PackageVersion | 1.1.2-1 |
| SHA-1 | A407E32FF6F8EDBDA9E76A446FB0E1E194214114 |
| SHA-256 | 61C0704B83C2C7FEF7C0DCE550C552B72B1F8852EDB347C25ABDDB25CAB2D1D9 |
| hashlookup:children-total | 86 |
| hashlookup:trust | 50 |
The searched file hash includes 86 children files known and seen by metalookup. A sample is included below:
| 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/python-cvxopt/examples/doc/chap9/acent |
| FileSize | 976 |
| MD5 | 9A4E0678B4C810C6A708A7754A3A616A |
| SHA-1 | 038F004A4D144EE9E886F83ACC7903A6835A67B6 |
| SHA-256 | D47245BFC5530F3BA38D8009B2E374EAB65B4FAC33ADB17E0F052F476F32FF91 |
| SSDEEP | 24:+PQA7N9Ez7Nwgw32MFEBCFENPMjnDM+iuZQrFHrcMP4ub:+PQey1wL32EZFaPMDDM+14FLxP4ub |
| TLSH | T1B51110447C52B02D437BC21984C61644D768254BCD0A687A3D5D47C0FF272E0E2B0B9A |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-cvxopt/examples/doc/chap10/roblp |
| FileSize | 514 |
| MD5 | ECC3259FFA27947B22A2DDFB2B62E7CB |
| SHA-1 | 07DF13023D1F3622CE91AA38DB09568098415ED7 |
| SHA-256 | B448C32AA4D92151BA81728D353E67A33AA8684F29636C0E55A4CAE4A4594B91 |
| SSDEEP | 12:He9z7NQWn7HBtu5nld7lyl/lZPGGg2IFaZ6N2F+BNSWEg:+97Nx7SZ/7lM3g6ZQ5bAg |
| TLSH | T1D4F00E09B850BCB09ABEE8548DCC1A103632368A0A713D02349C0789CFE87707EB3F89 |
| 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/pyshared/cvxopt/solvers.py |
| FileSize | 1481 |
| MD5 | 0050B28E427BED8D4CA2A5FC23D4910B |
| SHA-1 | 0DF0C1B75773F0858BCE6DD3300D6271A1242180 |
| SHA-256 | D03CDDCCE853AEB2881529E05DABA8542C1405DBD26A2718606BE3F7714706F0 |
| SSDEEP | 24:ec5T9Umc5UQboQtxFRzGfmx/4I7/q2SSCUGI7SiyUVOkHxhqTbV3njd7nU7eRx50:ec1am6UZScuwInPzGItyUjH6HRx5sN |
| TLSH | T13931421D4032F7BC09866285A746E44E5B2DE902F96B54003D4E9F5EE31AAB089DB5DC |
| 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/pyshared/cvxopt/cvxprog.py |
| FileSize | 83880 |
| MD5 | 27DD73834F94B4F934CA53AAD2254A3B |
| SHA-1 | 13D6B00A17864A1CBB0E6B7C51861661E1D1CFA8 |
| SHA-256 | BC0DF47B3BB63BC04684703F6A32F9D351BEB6B6D97895CABB97483872B8A6EC |
| SSDEEP | 768:cvEANa8dnu8v+f7YmE8krg0zPPnmEnzmBdiV/9d0FptPotraKzAr28dsFNemE8kx:58Yu48QpSv8Gv9TU |
| TLSH | T10783F907ED4266758723C83D889B4C941B2CD267550A6C383C5D93F82FA2B72D7B5BE8 |
| 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/pyshared/cvxopt-1.1.2.egg-info |
| FileSize | 838 |
| MD5 | 069DD76D3512781F8DCB8DF491298669 |
| SHA-1 | 1515D3287DB1AB01D7D4858D02DB0AEB38D42E9E |
| SHA-256 | CC11D8BE79D6BB997C62E3B9A5A5BF66FF5E5654513664F71EFCAE06A91110B5 |
| SSDEEP | 12:Dd175cw7dcZvAfC01dIx+8FwVwnr22q/3QGI8zWrQcF2NsQ1iFXMFwFKwUunXMlJ:Dd17WK2+8Rnrv6lBWc04sQLqbUKXMbv |
| TLSH | T1F401F1A1A6E1537505861AD7282825C09F354732346730C83C6C05153B93F2567BF3B9 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-cvxopt/examples/book/chap8/linsep.bin.gz |
| FileSize | 2217 |
| MD5 | 1D7805D2C20666FE849489694BAB6E80 |
| RDS:package_id | 182052 |
| SHA-1 | 1D3B0AE665F76E2519198224E2F0EE7BCF9329FF |
| SHA-256 | 56BAA06FBFB7CF2062C77FF616A44C07A9D9805EF2F31BB388C2EF698D739EFA |
| SSDEEP | 48:XWg7WWjSJISMDMPmlxxSnTbYqlx9QytwlcMDq0bK:fOmljTq5Qyt9Mm0m |
| TLSH | T148413B15CB37302764D6CFEDF7A8079F02A53B24808E43574D56D8927B03066EEB288E |
| insert-timestamp | 1679426071.644548 |
| source | RDS.db |