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GLPK is a freely available, open source, general-purpose LP/MIP solver. GLPK can solve small and medium-sized problems by means of an iterative active-set algorithm,
which by and large conforms to the paradigm of MIP-solvers. GLPK can also solve large-scale problems by using a combination of bisection search and the adaptive interior point algorithm
(see, for example, L. E. Gleser, “Numerical Analysis of Linear Programming Problems,” SIAM Journal on Computing, 12(1) (1981) 2). The intent of GLPK is to provide a
library of useful numerical routines that can be combined or adapted to solve particular LP or MIP problems.
GLPK Data Formats:
GLPK consists of a number of independent libraries. Most of the solver functions are independent and can be combined with other solvers without
modification of the function implementations themselves. In this regard, GLPK can be regarded as a collection of independent libraries.
To support the use of various solvers, GLPK can read data from and write data to a variety of data formats. The description of the data formats
follows below.
GLPK Data Formats:
[1] Input and Output Data Formats
[2] Data Formats for LP/MIP:
[3] Data Formats for Optimization:
[4] GLPK-Solver Library:
[5] Miscellaneous:
1) INTEGER TREE Data Format:
All GLPK functions that read data from or write data to an external file, such as the dopen(1) function or sqlsize(1), use a format
which is based on the INTEGER TREE format. This simple format can be constructed from a sequence of numbers. GLPK compresses this data into
a single integer that takes the minimum amount of space to store. This allows it to efficiently compress large files.
2) INTEGER TREE N-TREE Data Format:
All GLPK functions that access data structures with nested N-TREES use a format based on the INTEGER TREE format. GLPK
compresses an array of integers as an array of INTEGER TREE objects. This allows GLPK to efficiently store a N-TREE of
arbitrary depth.
3) ARRAY TREE Data Format:
GLPK functions that read data from or write data

Among the many open source packages available for linear programming, GLPK stands out as the most powerful general-purpose package.
Its capabilities are impressive, but it is also considerably more complex to use than the other two main packages (Gurobi and CPLEX).
GLPK achieves this level of functionality with remarkable robustness and efficiency.
It can be used in a wide variety of environments and is actively supported by a community of users.
GLPK is free software, designed for both academic and commercial use.
GLPK currently consists of the following components:
GLPK Core: A basic but powerful set of linear programming (LP) routines that provide basic support for mixed integer programming (MIP).
GLPK Constraints: A highly efficient package for modeling linear constraints.
GLPK Utilities: A set of computationally intensive routines used for high performance LP and MIP.
GLPK:GNU Library for Parcours Optimisation: A library for solving multi-objective constrained global optimization problems and for solving multi-criteria problems.
GLPK:GNU Library for Constrained Global Optimization: A library of basic routines and a toolkit for solving optimization problems with general global constraints.
GLPK:GNU Library for Mixed-Integer Programming: A library for solving mixed integer linear programming (MILP).
GLPK:GNU Library for Integer Optimization: A library for integer optimization problems (linear programming (LP) and MIP).
GLPK:GNU Library for Global Optimization: A library for solving general global optimization problems.
In this course, you will be introduced to the two applications of GLPK, namely Global Optimization and Constrained Optimization.
Global optimization is the search for an optimal solution for a general problem, and consists of an unconstrained or unconstrained mixed integer optimization problem.
Constrained optimization is the search for a “feasible” or optimal solution for a certain problem.
A feasible solution is a solution that respects the predefined constraints.
We will explore not only the algorithms and methods of GLPK for solving these two types of problems, but also some more advanced methods in unconstrained optimization, including RAPID and a simple to use method based on the LP-relaxation of the polynomial interpolation method.
Furthermore, the features of GLPK will be expanded in a practical application for model checking in which we will use it to solve a constrained set cover problem.
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GLPK is a multiprecision floating point nonlinear programming package with efficient algorithms.
It currently contains 18 different IP solvers, a library of high-speed algorithms, a number of efficient linear programming and nonlinear programming routines, and a collection of solver interfaces.
The philosophy of GLPK is the observation that many applications of nonlinear programming (NLP) are actually formulated as a type of linear programming problem (LPP) with complex objective functions and simple constraint sets.
GLPK is based on the GE System Integrated Toolset. Although the GE system is not free, it is very easily integrated with GLPK through a set of wrapper routines.
GLPK consists of a set of routines written in ANSI C that are organized in the form of a callable library.
The package is designed in order to help solve large-scale nonlinear programming (NLP), mixed integer nonlinear programming (MINLP) and other related problems.
GLPK Description:

GLPK is a multiprecision floating point nonlinear programming package with efficient algorithms.
It currently contains 18 different IP solvers, a library of high-speed algorithms, a number of efficient linear programming and nonlinear programming routines, and a collection of solver interfaces.
The philosophy of GLPK is the observation that many applications of nonlinear programming (NLP) are actually formulated as a type of linear programming problem (LPP) with complex objective functions and simple constraint sets.
GLPK is based on the GE System Integrated Toolset. Although the GE system is not free, it is very easily integrated with GLPK through a set of wrapper routines.
GLPK consists of a set of routines written in ANSI C that are organized in the form of a callable library.
The package is designed in order to help solve large-scale nonlinear programming (NLP), mixed integer nonlinear programming (MINLP) and other related problems.
GLPK Description:

GLPK is a multiprecision floating point nonlinear programming package with efficient algorithms.
It currently contains 18 different IP solvers, a library of high-speed algorithms, a number of efficient linear programming and nonlinear programming routines, and a collection of solver interfaces.
The philosophy of GLPK is the observation that many applications of nonlinear programming (NLP) are actually formulated as a type of linear programming problem (LPP) with complex objective functions and simple constraint sets.
GLPK is based on the GE System Integrated Tool

GLPK is a general-purpose LP and MIP solver. GLPK is implemented in ANSI C, C++, Fortran 90, Visual C and Visual Basic.
Detailed features:
GLPK is a good and popular C and C++ library. The solution to a problem with GLPK is usually faster and less error-prone compared to other solvers implemented in other programming languages.
GLPK is a free software released under the GNU GPL v2, and its latest version is V. 3.13.1. It supports Windows and Linux platforms.
GLPK library includes:
LP solver – LP(L)solve, SLICOTLP, DIMACS
MIP solver – GLPK-MIP, Gurobi, C-GLPK
LP and MIP heuristics – dantzig-Wagner, GLP-1, GLP-2, GLP-2U, GLP-2EU, GLPK-MIP
Constraint handling – GLPK, LP/MIP solver, SMT-COMPUTE, IPOPT
LpSolve package supports:
Windows
Solaris
Linux

MIP-GUROBI is a set of routines to solve mixed-integer programs using commercial solvers: Gurobi, Glpk and Cplex.
Besides all the features found in GLPK, MIP-GUROBI adds:
Integration of MIP-GUROBI with CPLEX and GLPK (MIP-Glpk & MIP-Gurobi)
Use of external software (integration with Xpress)
Scalar parametrization of decision variables
Use of ‘active’ and ‘unrestricted’ constraints
Use of a ‘cut’ compiler interface
Use of a’solver’ interface
Solving MIPs for a financial portfolio management is a potential use case for MIP-GUROBI.

GLPK-BOBYQA is a Python wrapper to gurobi solver that implements interface described in Chapter 3 of “The Python Algorithms Project” book. It has been implemented by Stefano Negri, ITRS research group, Brescia University, Italy.

GLPK-NP is a C/C++ library which is based on the GLPK package, including an interface to several commercial mixed integer programming solvers.
This package was developed

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OS: Windows 7
Processor: Intel i3 2.4 GHz / AMD Athlon X2 3.2 GHz or greater
Memory: 2 GB RAM
Graphics: Intel HD Graphics 4000 / AMD Radeon HD 7000 or greater
DirectX: Version 9.0c
Network: Broadband Internet connection
Storage: 20 GB available space
Other: Keyboard and mouse required
Recommended:
OS: Windows 8.1
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