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The General Algebraic Modeling System (GAMS) is a high-level modeling system for mathematical optimization. GAMS is designed for modeling and solving linear, nonlinear, and mixed-integer optimization problems. The system is tailored for complex, large-scale modeling applications and allows the user to build large maintainable models that can be adapted to new situations. Read more on Wikipedia...

- the GAMS Wikipedia page
- There are at least 810 GAMS repos on GitHub
- file extensions for GAMS include gms
- The Google BigQuery Public Dataset GitHub snapshot shows 43 users using GAMS in 49 repos on GitHub
- See also: Algebraic modeling language
- HTML of this page generated by LanguagePage.ts
- Improve our GAMS file

Example from the web:

```
*Basic example of transport model from GAMS model library
$Title A Transportation Problem (TRNSPORT,SEQ=1)
$Ontext
This problem finds a least cost shipping schedule that meets
requirements at markets and supplies at factories.
Dantzig, G B, Chapter 3.3. In Linear Programming and Extensions.
Princeton University Press, Princeton, New Jersey, 1963.
This formulation is described in detail in:
Rosenthal, R E, Chapter 2: A GAMS Tutorial. In GAMS: A User's Guide.
The Scientific Press, Redwood City, California, 1988.
The line numbers will not match those in the book because of these
comments.
$Offtext
Sets
i canning plants / seattle, san-diego /
j markets / new-york, chicago, topeka / ;
Parameters
a(i) capacity of plant i in cases
/ seattle 350
san-diego 600 /
b(j) demand at market j in cases
/ new-york 325
chicago 300
topeka 275 / ;
Table d(i,j) distance in thousands of miles
new-york chicago topeka
seattle 2.5 1.7 1.8
san-diego 2.5 1.8 1.4 ;
Scalar f freight in dollars per case per thousand miles /90/ ;
Parameter c(i,j) transport cost in thousands of dollars per case ;
c(i,j) = f * d(i,j) / 1000 ;
Variables
x(i,j) shipment quantities in cases
z total transportation costs in thousands of dollars ;
Positive Variable x ;
Equations
cost define objective function
supply(i) observe supply limit at plant i
demand(j) satisfy demand at market j ;
cost .. z =e= sum((i,j), c(i,j)*x(i,j)) ;
supply(i) .. sum(j, x(i,j)) =l= a(i) ;
demand(j) .. sum(i, x(i,j)) =g= b(j) ;
Model transport /all/ ;
Solve transport using lp minimizing z ;
Display x.l, x.m ;
$ontext
#user model library stuff
Main topic Basic GAMS
Featured item 1 Trnsport model
Featured item 2
Featured item 3
Featured item 4
Description
Basic example of transport model from GAMS model library
$offtext
```

Example from Wikipedia:

```
Sets
i canning plants / seattle, san-diego /
j markets / new-york, Chicago, topeka /聽;
Parameters
a(i) capacity of plant i in cases
/ seattle 350
san-diego 600 /
b(j) demand at market j in cases
/ new-york 325
Chicago 300
topeka 275 /聽;
Table d(i,j) distance in thousands of miles
new-york Chicago topeka
seattle 2.5 1.7 1.8
san-diego 2.5 1.8 1.4 ;
Scalar f freight in dollars per case per thousand miles /90/聽;
Parameter c(i,j) transport cost in thousands of dollars per case聽;
c(i,j) = f * d(i,j) / 1000聽;
Variables
x(i,j) shipment quantities in cases
z total transportation costs in thousands of dollars聽;
Positive Variable x聽;
Equations
cost define objective function
supply(i) observe supply limit at plant i
demand(j) satisfy demand at market j聽;
cost .. z =e= sum((i,j), c(i,j)*x(i,j))聽;
supply(i) .. sum(j, x(i,j)) =l= a(i)聽;
demand(j) .. sum(i, x(i,j)) =g= b(j)聽;
Model transport /all/聽;
Solve transport using lp minimizing z聽;
Display x.l, x.m聽;
```

title | authors | year | publisher |
---|---|---|---|

Practical Financial Optimization: A Library of GAMS Models | Nielson, Soren S and Consiglio, Andrea | 2010 | Wiley-Blackwell |

Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology | Neculai Andrei | 20171204 | Springer Nature |

Continuous Nonlinear Optimization For Engineering Applications In Gams Technology | Andrei, Neculai (author.) | Springer International Publishing : | |

Nonlinear Optimization Applications Using The Gams Technology (springer Optimization And Its Applications) | Neculai Andrei | 2013 | Springer |

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