A Python binding to GLPK already exists in the form of Python-GLPK, but as it is automatically created through SWIG it is not very Pythonic.
Building this module requires that the user have installed the GLPK and GMP libraries. The module builds and appears to work on my simple test files in Python 2.3, 2.4, and 2.5. Earlier versions of Python will not work.
In the ideal case (e.g., you have GLPK and GMP libraries and headers installed in default search paths), one would install this through
make install, and be done.
The section on Building and Installing has more information.
To use the C API, one would first
#include "glpk.h", create an LPX structure through
lpx_create_prob(), and thence manipulate this structure with
lpx_* functions to set the data, run the optimization, and retrieve the desired values.
To use this Python module, one would import the
glpk module, create an LPX Python object through
glpk.LPX(), and thence manipulate this object and the objects it contains to set the data, run the optimization, and retrieve the desired values. The Python module calls the C API to support these operations.
PyGLPK has objects floating around everywhere, and very few actual methods. Wherever possible and sensible, one gets and sets traits of the problem by accessing a method and directly assigning those traits. An example of this showing how PyGPLK works and how it does not work might be interesting.
|PyGLPK is Like This||It Isn't Like This|
lp.maximize = True lp.cols.add(10) for col in lp.cols: col.name = 'x%d' % col.index col.bounds = 0.0, None lp.obj[col.index] = 1.0 del lp.cols[2,4,5]
lp.set_maximize() lp.add_cols(10) for cnum in xrange(lp.num_cols()): lp.set_col_name(cnum, 'x%d' % cnum) lp.set_col_bounds(cnum, 0.0, None) lp.set_obj_coef(cnum, 1.0) lp.del_cols([2,4,5])
Both design strategies would accomplish the same thing, but there are advantages in the first way. For example, if I tell you only that columns of an LP are stored in a sequence
cols, for free you already know a lot (assuming you're familiar with Python):
Unlike the C API, everything is indexed from 0, not 1: the user does not pass in arrays (or lists!) where the first element is ignored. Further, one indexes (for example) the first row by asking for row 0, not 1.
In the comparative examples of parallel C and Python code, wherever possible and appropriate I sprinkle
+1 in the C code. Of course, only a lunatic would really write code that way, but I do this to highlight this difference, and second, make it more obvious which portions of C and Python correspond to each other: it's far easier to see the relation between
[7, 3, 1, 8, 6] and
[7+1, 3+1, 1+1, 8+1, 6+1], versus
[8, 4, 2, 9, 7].
PyGLPK also honors Python's quirk of "negative indexing" used to count from the end of a sequence, e.g., where index -1 refers to the last item, -2 second to last item, and so forth. This can be convenient. For example, after adding a row, you can refer to this row by
lp.rows[-1] rather than having to be aware of its absolute index.
The GLPK's approach to errors in arguments is deeply peculiar. It writes an error message and terminate the process, in contrast with many APIs that instead set or return an error code which can be checked. The PyGLPK takes the more Pythonic approach of throwing catchable exceptions for illegal arguments. Unfortunately, to avoid the process being terminated, this requires that absolutely every argument be vetted, requiring that PyGLPK have the additional overhead of doing sometimes rather detailed checks of arguments (which are, of course, checked yet again when the GLPK has access to them). It seems unlikely that GLPK's design will be improved in this area.
To ground you, we show an example of the module's workings. (This example is covered in more detail in the examples section.) Taking the introductory example from the GLPK C API reference manual, we start with the following example linear program:
|maximize||Z||=||10 x0||+||6 x1||+||4 x2|
|and bounds of variables||
In the following, we show Python code to define and solve this problem, and subsequently print out the objective function value as well as the primal values of the structural variables.
import glpk # Import the GLPK module lp = glpk.LPX() # Create empty problem instance lp.name = 'sample' # Assign symbolic name to problem lp.obj.maximize = True # Set this as a maximization problem lp.rows.add(3) # Append three rows to this instance for r in lp.rows: # Iterate over all rows r.name = chr(ord('p')+r.index) # Name them p, q, and r lp.rows.bounds = None, 100.0 # Set bound -inf < p <= 100 lp.rows.bounds = None, 600.0 # Set bound -inf < q <= 600 lp.rows.bounds = None, 300.0 # Set bound -inf < r <= 300 lp.cols.add(3) # Append three columns to this instance for c in lp.cols: # Iterate over all columns c.name = 'x%d' % c.index # Name them x0, x1, and x2 c.bounds = 0.0, None # Set bound 0 <= xi < inf lp.obj[:] = [ 10.0, 6.0, 4.0 ] # Set objective coefficients lp.matrix = [ 1.0, 1.0, 1.0, # Set nonzero entries of the 10.0, 4.0, 5.0, # constraint matrix. (In this 2.0, 2.0, 6.0 ] # case, all are non-zero.) lp.simplex() # Solve this LP with the simplex method print 'Z = %g;' % lp.obj.value, # Retrieve and print obj func value print '; '.join('%s = %g' % (c.name, c.primal) for c in lp.cols) # Print struct variable names and primal values
This may produce this output.
Z = 733.333; x0 = 33.3333; x1 = 66.6667; x2 = 0