Key | Value |
---|---|
FileSize | 1459010 |
MD5 | A061B251BC425E1FAD049BD35DC6067E |
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 | 1.1.3-2fakesync1 |
SHA-1 | 095F4FFA0DD263541197AEA730B2712AF42855D0 |
SHA-256 | 8042A77E464009CEA17343E0438D4FE0F8696B31486211524AB8E8DDE2162F5A |
hashlookup:children-total | 88 |
hashlookup:trust | 50 |
The searched file hash includes 88 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/lib/python2.7/dist-packages/cvxopt/fftw.so |
FileSize | 26600 |
MD5 | A467F6C6F606C4BC272E4ADA21852ED6 |
SHA-1 | 0456A600B59142CDA78539A6F09DBE86A083C8B0 |
SHA-256 | E0991603885E8CFB90299B1F5542ADCFCE325655F3D9E4B4D87EAF7B28112238 |
SSDEEP | 768:KmxnChf/nnEDdEvni7PAjX66pfVgLNrMWky/:X63Ba7IjX6UfVgL |
TLSH | T176C2E9057AC241F6F5230070D696AB6B2631A203516ECBF3F61C196DFB62A310F657AF |
Key | Value |
---|---|
FileName | ./usr/share/pyshared/cvxopt/msk.py |
FileSize | 30303 |
MD5 | BE75B79CF584BCE72E192EBA9F18795C |
SHA-1 | 0973A4BF52618DFC6A43690840B0A405B77DFE6A |
SHA-256 | A79578F461864CEDF023EEB5E62E086A6FF384DBC9F33772396BEC757B9A5A95 |
SSDEEP | 384:K7R5stI/7kyl/sdF48C/eQsweUAq/MlXiI/l0kwCkHI/yr:Uvstkgyl/sdF48ODsweiaXik6kXkHkW |
TLSH | T11CD2830558400D3AA2A3817D8CD7CC097F6195633D8B2D7A386C91B86F1B737D7B87AA |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/book/chap6/regsel.py |
FileSize | 3149 |
MD5 | 437919296D51AC8F69D12500C0807F42 |
SHA-1 | 097BB7690701B72F1ABB505DCC1016A792BE9EC9 |
SHA-256 | 24429B539050989ACBB789680C3E5E10574C938435FDFAB373A23F7C0BA58B01 |
SSDEEP | 96:S3HRpoF7BL4lCI1wz9WAdB+73DXLv18byir:G7lCvRK3Db98bye |
TLSH | T142516544F4423C76865BD1A9E4D134204F2AD4272D1F289AFFAE3E888F478F54674389 |
Key | Value |
---|---|
FileName | ./usr/lib/python2.7/dist-packages/cvxopt/cholmod.so |
FileSize | 292676 |
MD5 | CDC011275487C6E27B642342B67068E1 |
SHA-1 | 0E910D05D2EC0DE5BB201697C2B76745E22639F7 |
SHA-256 | 239E7AC9623062D04EA816BEB0E953B822DD4DB682696B6BA0A38B6303916AF4 |
SSDEEP | 6144:YLV5ZJWgDt/k/OLx71SvR9SvXAywAco29+gUtwg4saR:eVFWgDhk/Qxm8XKM7w/ |
TLSH | T17154C754EACB40F0CA5708708567F62F9B21AB07C119E9A1FB696F13F737B267D18218 |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/book/chap6/consumerpref.py |
FileSize | 3991 |
MD5 | 5CCD74DD7D1E5423D4C8C18F3FEFCC2F |
SHA-1 | 0FB481D08091F23B0622C13CC9F8ABFB75FF9BC3 |
SHA-256 | 806DD9B8F6944C06A04A87D8FB2F986747E900D0136FFCFB5F40CA007D98891B |
SSDEEP | 96:AJOmZEEFIXIZJj0SRQwC5Aw212g0SRojzhze:AImWCIXIZJj0S+wCCXgg0SiVe |
TLSH | T15D81EC03127AE9344A07D7FEE5913310A92DD8EB6D1A3886306E0E875F175ED6232D5F |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/doc/chap9/floorplan.py.gz |
FileSize | 1507 |
MD5 | 3D46A3E0091B6C5456B860784E4ED58B |
SHA-1 | 10B5C849601281E41F0E47BEA7D21D09AAE4391A |
SHA-256 | B3D430325563926C6520EFC4BB2EF20814B6811390A6A6D86832690D27874061 |
SSDEEP | 24:Xby2zMH0eOxdTGRnLyIkbUNICSbQ8bd9iwZgbd0a5YmStsd/8y7BPL7wvBT7:XbrwitG1vNuQ0iw0+a5HSQ/z7Za7 |
TLSH | T1FC310A927C3839084128FE4A0E861EEF602EC01F68A120A1347E301B9F096C780E53E9 |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/doc/chap8/conelp.py |
FileSize | 815 |
MD5 | EA54E397E3093B100C5BBF14C3A4E06D |
SHA-1 | 14BC88EA42FD67A8DCA767FADCAAC178D8332816 |
SHA-256 | 949E33AE7959C59A90D10159DA32363FDD36BB226DE58E1F3A4569FB46433DA2 |
SSDEEP | 12:UIFEa7NQBQMLt1YcTYeul+RHbq4Gn+1G2rAoDo29gzKaneSnYtfszECro:UI57NEnZful+xua/A8mKank2vc |
TLSH | T1AC01D49F91873CF4432E8B74A0471D1467B6C83979D73188387E8E2DAB3DB8496ACE54 |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/book/chap7/expdesign.py.gz |
FileSize | 1632 |
MD5 | B92636BDEC1E72ED7947008F204DB178 |
SHA-1 | 152161D93560EC30BFAFA8017701AB5241DBAAD3 |
SHA-256 | AE8841BD9EE34A4791315BAC1D324BE5002ED9465C3E62DA0E635996D5371B38 |
SSDEEP | 24:X0rQx03RUiO+idzDxx0/mkPjclwGWdxE/ABToGpwVxf/La9CYIs2P:XmQu3NidztSdbciGIxAWTPwVg5Q |
TLSH | T1CE310A0790A731969639DD3EE84A5619306734F79C268ABC65782439B15322A3159339 |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/doc/chap8/coneqp.py |
FileSize | 614 |
MD5 | 46E747A1F071EA1BD1DCEAA534FFB4BC |
SHA-1 | 154D12C7989123F054D2B63E021B3A17E17B3958 |
SHA-256 | FC8AE59A551F2C6568F26EC693C2DF1A26109650963664F48AA2CC1E858F1443 |
SSDEEP | 12:BMztjXK/sOf7NQULrVlBE9I81NaJnjkar4nkMIal3nV7:0tjnC7NfPB+I8ijkdkMlF7 |
TLSH | T102F02D4BC407B85CE6B4E029981A1C950E79CD086D2EB0003D3E54A18FBA262CE35756 |