| Key | Value |
|---|---|
| FileSize | 1703660 |
| MD5 | F59691EABDC079D5BEAA16D74E579585 |
| 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 | 4693EA9C37BB9BC0521F88C2E535D4CDF86AC870 |
| SHA-256 | 190DDB6A82311FE63AF3CEF98804B84BAA20AB1C4DF2BDB2CA8F8121515AB728 |
| 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/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/python2.5/site-packages/cvxopt/cholmod.so |
| FileSize | 285596 |
| MD5 | 34CD2476E396B3BEE256B5AD6012D896 |
| SHA-1 | 0D48C2CE5E8DBE3DBF4B6938506D352683A96897 |
| SHA-256 | D4941FF9D41BCFD15994F5FD28FFDBAFB6609EAC97A3EDD837EF8D00D467479B |
| SSDEEP | 6144:ohVUR/d9wzFXlMqBh36a9lfUzi5MeyZ++bRVeHTv+tyJq65+R:vfwzFXlFhphMPbjQTzb |
| TLSH | T1A1541716DB56A2E7D48304B0670BE7FB46117332D558A8CABB545F0CAB36CE5AB1CB03 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.5/site-packages/cvxopt/umfpack.so |
| FileSize | 471712 |
| MD5 | 26E011327E1C118947E055EC721FAE03 |
| SHA-1 | 11B7AB7CD098121BF6252356E1123AAD59071EB3 |
| SHA-256 | 4FE1FF77203FB1C6D4BC92A32C05942E5E0101AC28F3EDA4306842B8185B67EA |
| SSDEEP | 12288:ThsYC99Y0oHcUhhuDokjg6kGob+DshV5XTh8:T349poHcUhhkjU6kGOksV598 |
| TLSH | T1B0A45B46EE57F29BC45600706346A3B754207B34A894FC9ABB85EE1CF632EE5720DB13 |
| 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/lib/python2.4/site-packages/cvxopt/fftw.so |
| FileSize | 8416 |
| MD5 | 8A38824817659F82A2A6A33C79C65DEC |
| SHA-1 | 13BB0EFD58F655914241D2D22A9AB11F6FF3BB0B |
| SHA-256 | D1EBCBC4411A8F6912BB74007AECE6E13727C433E96EE1D90C214C8D055F35BC |
| SSDEEP | 192:UDE1eE34X3QgXCXaZkbIiUqaTCWnd/UrLoIWNaZ2aJ:qEn6ggXCXaZkECaT7d/9Nss |
| TLSH | T17602D797F64286B1D41300F08A9BC91F472CD580504B8B63B76C587AAFA3A81BF39A5D |
| 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 |