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Integer programming is NP-complete. In particular, the special case of 0–1 integer linear programming, in which unknowns are binary, and only the restrictions must be satisfied, is one of Karp's 21 NP-complete problems. [1] If some decision variables are not discrete, the problem is known as a mixed-integer programming problem. [2]
Here is a little sample model written in MPS format (explained in more detail below): NAME TESTPROB ROWS N COST L LIM1 G LIM2 E MYEQN COLUMNS XONE COST 1 LIM1 1 XONE LIM2 1 YTWO COST 4 LIM1 1 YTWO MYEQN -1 ZTHREE COST 9 LIM2 1 ZTHREE MYEQN 1 RHS RHS1 LIM1 5 LIM2 10 RHS1 MYEQN 7 BOUNDS UP BND1 XONE 4 LO BND1 YTWO -1 UP BND1 YTWO 1 ENDATA
solver for mixed integer programming (MIP) and mixed integer nonlinear programming (MINLP). SciPy: BSD general numeric package for Python, with some support for optimization. Uno: MIT Lagrange-Newton solver that unifies nonconvex optimization, implemented in C++. Developed at Argonne National Laboratory and Zuse Institute Berlin. [3]
All computations are performed in exact integer arithmetic using GMP or imath. Many program analysis techniques are based on integer set manipulations. The integers typically represent iterations of a loop nest or elements of an array. isl uses parametric integer programming to obtain an explicit representation in terms of integer divisions.
HiGHS is open-source software to solve linear programming (LP), mixed-integer programming (MIP), and convex quadratic programming (QP) models. [1] Written in C++ and published under an MIT license, HiGHS provides programming interfaces to C, Python, Julia, Rust, R, JavaScript, Fortran, and C#. It has no external dependencies.
The integer factorization problem is in NP and in co-NP (and even in UP and co-UP [23]). If the problem is NP-complete, the polynomial time hierarchy will collapse to its first level (i.e., NP = co-NP). The most efficient known algorithm for integer factorization is the general number field sieve, which takes expected time
Parametric programming is a type of mathematical optimization, where the optimization problem is solved as a function of one or multiple parameters. [1] Developed in parallel to sensitivity analysis , its earliest mention can be found in a thesis from 1952. [ 2 ]
In the field of mathematical optimization, stochastic programming is a framework for modeling optimization problems that involve uncertainty. A stochastic program is an optimization problem in which some or all problem parameters are uncertain, but follow known probability distributions .