Key | Value |
---|---|
FileSize | 1669268 |
MD5 | C15C1518DF856441D6F095C0CD0050AC |
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 | 0.9.3-1 |
SHA-1 | 3E8B0677EB95B5329C3CCA180DC822426A4B3A24 |
SHA-256 | E221ABA62E593B2A1C0F7A402346DB9C0586A10D439535CC27F6D01C86397014 |
hashlookup:children-total | 95 |
hashlookup:trust | 50 |
The searched file hash includes 95 children files known and seen by metalookup. A sample is included below:
Key | Value |
---|---|
FileName | ./usr/lib/python2.4/site-packages/cvxopt/lapack.so |
FileSize | 169524 |
MD5 | 685C7EDA7F521575DA02DE9D62B7012D |
SHA-1 | 0021E27108E1326F73F7B25C515C0043A371AE70 |
SHA-256 | 0AFB92352592D235BA8F519539B513C3C190204BB2E8528DC05968694C3EE5B9 |
SSDEEP | 1536:NwM2GwtNNgNQaGh//X/vb9gCgolIZWIvY3RbEDA1CYlDwDNtTGD:NZ6tcQaGh3X/z6Cgo6ZRCn1CYlDCNJk |
TLSH | T1A3F3D7B33B712238C410967A61672DB3EA3C43512950FB67BADD1E2C3F96DC01A63766 |
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 | 890 |
MD5 | 0FB96DD2F91C37F8A9A144F3DA1A7663 |
SHA-1 | 06BE23FC1601D7892A63010AD7506380013AFD05 |
SHA-256 | 55CBE032A18BAC3E24290F5EC25C17FF85C9FB61DB889824E1BE1E9617B8BEE5 |
SSDEEP | 24:6tAyp0aJbWGfxyq8ciKKSuCYqZxpNqDEx6AYqLNm438SsqFdVlE:QKCbuq8ciKBYKzDxfYaNm4TjFdVlE |
TLSH | T17211560C916235BC551271CDD78119104F3815A9720B63854C7840B333C79E9937F3EB |
Key | Value |
---|---|
FileName | ./usr/lib/python2.4/site-packages/cvxopt/gsl.so |
FileSize | 7864 |
MD5 | 743CAE13021D7BE8B1E8E27E3F5BDB83 |
SHA-1 | 088376F5DA3C5D60875ACE565A31F57DD0B9AC05 |
SHA-256 | EC848E7B7B2BDEED79534CD4B619FB3D4B48647F5193444D91381EE86EA6BD1B |
SSDEEP | 96:rJK1tQJ6ZscsTuIu1lDEFkUW+iSXvzFeeBVYTtDcHZUvbXAV1Y726oP0SPX3uX+N:rJK1a9ZCIu1REOoR7zSwn4BOEa |
TLSH | T107F12EE777A29E36C4E41A3880EB4713671C8A0099A68367B18D85663FC66603D3B7D2 |
Key | Value |
---|---|
FileName | ./usr/lib/python2.5/site-packages/cvxopt/dsdp.so |
FileSize | 21472 |
MD5 | 8738D5F27CA835C3D819F7781F5DEB17 |
SHA-1 | 0C5CF429A11444D028BF8C4A1B0B808B12DDC4EA |
SHA-256 | E36C7EE81637A8E2489CB4247AACB2FF7FB31FC9B77061E5DFAAFC2D9E89EFE9 |
SSDEEP | 384:8XhptwS7s6euBLofg4xkwbr6J9NVs6XYXeBQEtle:8RTwSJBegRAyC6XYJn |
TLSH | T1A8A2D75337992A37C4D01A7481E3C7336B5F4B446585624F3D9E081F2F9AA912E73B86 |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/doc/chap10/roblp |
FileSize | 507 |
MD5 | 8E97815A4E277B3CD1E8D952AE976FC2 |
SHA-1 | 13924DCE887C4C503A497F2B2471D4B664A18D0B |
SHA-256 | 7AA7BB6CECAC6D5E26EB1A85141E4ED74C6ABD98C29B54BE28D460862EE5F09D |
SSDEEP | 12:He9v7DWn7HBtu5nld7lyl/lZPGGg2IFaZ6N2F+BNSWEg:+N7u7SZ/7lM3g6ZQ5bAg |
TLSH | T1D5F00E0D7400BCB09ABAD8D08CCC1A103672228B0A713D03345C47CACBB86707EB3F86 |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/doc/chap10/lp |
FileSize | 1115 |
MD5 | D616F95108828979A9C44CE61EAE879A |
SHA-1 | 16ED4BF0622E105A1B1781B066FA35C9EF9FE0F2 |
SHA-256 | D6C66D7E487AD150B8F0CB16C62C9DCACD3D62D1693673D73780F16D3883C9E1 |
SSDEEP | 24:+dI17kF7SqKtpy9L0dGACrYuUw9vu0Trfk:+KyMqKtKeGLrzUw9vuJ |
TLSH | T1B3216B0EBDCB70248D7984CED4ED06552AA2426C29F33EA3A61DDFB29091A55403BB59 |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/book/chap6/tv.gz |
FileSize | 2437 |
MD5 | F7475C1946A0D74AC412CDBBB943605E |
SHA-1 | 18051E725550642CCA5F542C99011F6DB173AAAC |
SHA-256 | 964D4EE828CB12C957BFF51EA7D8041B53BBAF959372B4E3BCDEC27CFACBB499 |
SSDEEP | 48:X2i6c9cavq2ASqzdcQKka5LMSRCTba2+oJvASdERK02zZrXD1Gul:ptVBAbSQKka5bo31Z9Ec02zph1l |
TLSH | T1A8414C1648EA6002991C08FDC059ED15950909DEF7264B19290193B976720EFB33353F |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/doc/chap10/normappr |
FileSize | 685 |
MD5 | E749E8DF21478A2B5B7AF27C62AD7A19 |
SHA-1 | 185809A572BBFE878F7C6B256785A71D1CAD4D82 |
SHA-256 | 8EDBE8ACF65DD52BE582FAA32538CA8512A76062C4B4A838D341EBD1724E8D81 |
SSDEEP | 12:HeNSnjo7DWQ7HBtuOWuNhYrkffigmxKkTDb2o6vVSRRxGH5snfwGPYsgwG/slww:+NUjo7V7SJuNOwfqgmxKyGlvI8q+AH |
TLSH | T1DE017B0CB521B9012BC7CAE9D1DC2B508F7011A2092C64A534AA2F810F6B6B87CBADD6 |
Key | Value |
---|---|
FileName | ./usr/share/doc/python-cvxopt/examples/doc/chap8/coneqp |
FileSize | 631 |
MD5 | C0E6F073B7B50BA296F1A3A9110EDFD4 |
SHA-1 | 1D0FB21D69B50F8DE68240FF2609E7D8587DA0FD |
SHA-256 | D5D9EE638642FC53440F9D986A3CB821AE00252200AFE9318F40ED72D65FEEA6 |
SSDEEP | 12:HYGtjXK/sOf7NQtFI7DoAVlBE9I81NaJnjkar4nkMIal3nV7:4GtjnC7NyFI7kmB+I8ijkdkMlF7 |
TLSH | T1F2F0F94FC006B868E6B4E02A89192C910F759D086D2BB400393E55A08FAE2A2CD75755 |