GAMSPy for GAMS Users#

This document is for GAMS users who are interested in translating their existing GAMS models to GAMSPy or vice versa.

Translating Symbols#

The way to create symbols such as Set, Alias, Parameter, Variable, and Equation is explained in their respective documentation pages and you can see an example for the creation of each symbol below:

import gamspy as gp

m = gp.Container()

i = gp.Set(m, 'i', records=['i1','i2'])
a = gp.Alias(m, 'a', alias_with=i)
p = gp.Parameter(m, 'p', domain=i, records=[['i1','1'], ['i2','2']])
v = gp.Variable(m, 'v', domain=i)
z = gp.Variable(m, 'z')
e = gp.Equation(m, 'e', domain=i)
e[i] = v[i] + p[i] <= z
my_model = gp.Model(m, equations=[e], problem="lp", sense="min", objective=z)
my_model.solve()

Set i / i1, i2 /;
Alias (i, a);
Parameter p(i) / i1 1, i2 2 /;
Variable v(i), z;
Equation e(i);
e(i) .. v(i) + p(i) =l= z
Model my_model / e /;
solve my_model using LP min z;

Translating Operations: Sum/Product/Smin/Smax#

Frequently used GAMS operations which accept an index list and an expression can be translated as follows:

from gamspy import Sum, Product, Smin, Smax

m = gp.Container()
i = gp.Set(m, 'i', records=['i1','i2'])
a = gp.Parameter(m, 'a', domain=i, records=[['i1','1'], ['i2','2']])
z = gp.Variable(m, 'z')

eq = gp.Equation(m, name="eq")
eq[...] = Sum(i, a[i]) <= z

Set i / i1, i2 /;
Parameter a(i) / i1 1, i2 2 /;
Variable z;
Equation eq;
eq .. sum(i, a(i)) =l= z;

Card/Ord#

Card and Ord operations can be translated as follows:

import gamspy as gp
import math

m = gp.Container()
i = Set(m, name='i', records=[f'i{i}' for i in range(181)])
step = Parameter(m, name="step", records=math.pi / 180)
omega = Parameter(m, name="omega", domain=i)
omega[i] = (Ord(i) - 1) * step

Set i / i0*i180 /;
Parameter step;
step = pi / 180;
Parameter omega(i);
omega(i) = (ord(i) - 1) * step;

Domain#

This class is exclusively for conditioning on a domain with more than one set.

import gamspy as gp

m = gp.Container()
bus = gp.Set(m, name="bus", records=[f"i{b}" for b in range(1, 7)])
node = Alias(m, name="node", alias_with=bus)
conex = Set(m, name="conex", domain=[bus, bus])

branch = Parameter(m,"branch", [bus, node, "*"] ,records=...)

p = Parameter(m, name="p")

conex[bus, node].where[branch[bus, node, "x"]] = True
conex[bus, node].where[conex[node, bus]] = True

p[...] = Smax(
    Domain(bus, node).where[conex[bus, node]],
    branch[bus, node, "bij"] * 3.14 * 2,
)

Set bus / i1*i6 /;
Alias (bus, node);
Set conex(bus, bus);

Parameter branch(bus, node, "*") / ...... /;
Parameter p;

conex(bus, node)$(branch(bus, node, "x")) = yes;
conex(bus, node)$(conex(node, bus)) = yes;

p = smax((bus, node) $ (conex(bus, node)), branch(bus, node, "bij") * 3.14 * 2)

Number#

This is for conditions on numbers or yes/no statements.

import gamspy as gp

m = gp.Container()
i = gp.Set(m, "i", records=range(1,5))
ie = gp.Set(m, "ie", domain=i)
x = gp.Variable(m, "x", domain=i)
ie[i] = gp.Number(1).where[x.lo[i] == x.up[i]]

Set i / 1*4 /;
Set ie(i);
Variable x(i);
ie(i) = yes$(x.lo(i) = x.up(i));

math package#

This package is for the mathematical operations of GAMS.

from gamspy import Container, Set, Variable
import gamspy.math as gams_math
import random

m = Container()
i = Set(m, "i", records=['i1', 'i2'])
k = Set(m, "k", records=['k1', 'k2'])
sigma = Variable(m, name="sigma", domain=[i, k], type="Positive")
sigma.l[i, k] = gams_math.uniform(0.1, 1) # Generates a different value from uniform distribution for each element of the domain.
print(sigma.records)
sigma.l[i, k] = random.uniform(0.1, 1) # This is not equivalent to the statement above. This generates only one value for the whole domain.
print(sigma.records)

Set i / i1, i2 /;
Set k / k1, k2 /;
Positive Variable sigma(i,k);
sigma.l(i,k) = uniform(0.1, 1);
display sigma.l;

Logical Operations#

Since it is not possible in Python to overload keywords such as and, or, and not, you need to use bitwise operatiors &, |, and ~.

Mapping:

  • and -> &

  • or -> |

  • not -> ~

error01[s1, s2] = rt[s1, s2] & (~lfr[s1, s2]) | ((~rt[s1, s2]) & lfr[s1, s2])

error01(s1,s2) = rt(s1,s2) and not lfr(s1,s2) or not rt(s1,s2) and lfr(s1,s2);

Translating GAMS Macros#

Macros in GAMS can be translated to GAMSPy as functions. The following example shows how GAMS Macro reciprocal can be defined as a function in Python to be used in GAMSPy:

import gamspy as gp

def reciprocal(y):
    return 1 / y

m = gp.Container()
z = gp.Parameter(m, "z")
x1 = gp.Parameter(m, "x1", records=2)
x2 = gp.Parameter(m, "x2", records=3)
z[:] = reciprocal(x1) + reciprocal(x2)
print(z.records)

$macro reciprocal(y) 1/y

scalar z, x1 /2/, x2 /3/;

z = reciprocal(x1) + reciprocal(x2);
display z;

Automatic Conversion of a GAMSPy Model to GAMS#

Existing GAMSPy models can be translated to a GAMS model automatically by using Model.toGams:

import gamspy as gp

m = gp.Container()
i = gp.Set(m, "i", records=["i1", "i2"])
a = gp.Parameter(m, "a", domain=i)
...
...
your_model = gp.Model(m, "your_model", equations=...)
...
...

your_model.toGams(path=<gams_model_path>)

The generated GAMS model can be found under <gams_model_path>/<model_name>.gms. If you also want to generate a pf file that contains the necessary option, you can provide it with options argument. If you want to dump the GAMS state, you can also set dump_gams_state to True.