| Key | Value |
|---|---|
| FileSize | 2264658 |
| MD5 | 47F8B7C543B84C085500FDFBFEB88452 |
| 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 | EE834BCC343450BD5E4D04625440402D3A89AF50 |
| SHA-256 | 77018718E6D6272C56E2E7F42EDDD64ACAA368D4AEF0D958D4AFD6F69F6E8880 |
| 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/python2.4/site-packages/cvxopt/dsdp.so |
| FileSize | 33032 |
| MD5 | F7BA9A16C3BC46A01D2EF730459D9CE0 |
| SHA-1 | 0AA3ADBB27C9B338DC11A5EBE87717D4C7CB9F0E |
| SHA-256 | 207EA1BA21AB8B13F005D0E43B0E89FFDAEB68C4E542A16E235C7E1E7DB1128B |
| SSDEEP | 384:x0jhanT+/l2H6pz93MoOhiKwnO0FH/wuKIbVppA4XFU2so1XeBtN:x0NZ/l2sB3Mo+iKwnT4uHbVHsL0 |
| TLSH | T146E290537B42597BC46E23B242D3235D07BBE1658363435BB33A82303F6778A8F1A295 |
| 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/lib/python2.5/site-packages/cvxopt/fftw.so |
| FileSize | 20544 |
| MD5 | 5E25FE1905B93FBD6891F22685A69074 |
| SHA-1 | 188E3E4EFE79BBB2C345C98BC5208AB7359D72B3 |
| SHA-256 | 9CCC0DBE23BD6CEDC067B6EB82C0B6F186273BF8339BE500CFB78C41A424D0C0 |
| SSDEEP | 384:i2Y+3G39tfr+Rz0Ny3ZkEpL2EFLNMMxWA+:ZG3ffrspkEs |
| TLSH | T110926FD22B031A77C01E03B283EB536A1779E25683D34B7B677891383F8774A5F216A5 |
| 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 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.4/site-packages/cvxopt/umfpack.so |
| FileSize | 932088 |
| MD5 | DAA932A63F9AA2A9DB7929885AA8DBBD |
| SHA-1 | 1E2CE8C75C3D7C12428605462B45122F6A125F08 |
| SHA-256 | 1B7D6E1E32C43311F8EFCED0BD47B0D74C9101C0D6B5782D5D4BA7F8336B9178 |
| SSDEEP | 12288:9skx1K+in4okrQY/Xnc/YxCC+4dFGlYT3YiicoVy6f6U89UBoY3WEX0y:9Bxk769M |
| TLSH | T18515D8A39B8B2AA7D87603B382D743695B36F25907974F36533C42283DDB39E1E12705 |