def __del__(self):
if hasattr(self, 'lp') and self.lp is not None:
if not isinstance(self.lp, tuple):
glpk.glp_delete_prob(self.lp)
self.lp = None
def test_swiglpk(self): """Test the underlying GLPK lib and its SWIG interface based on the example from https://github.com/biosustain/swiglpk """ ia = glp.intArray(1 + 1000) ja = glp.intArray(1 + 1000) ar = glp.doubleArray(1 + 1000) lp = glp.glp_create_prob() smcp = glp.glp_smcp() glp.glp_init_smcp(smcp) smcp.msg_lev = glp.GLP_MSG_ALL # use GLP_MSG_ERR? glp.glp_set_prob_name(lp, "sample") glp.glp_set_obj_dir(lp, glp.GLP_MAX) glp.glp_add_rows(lp, 3) glp.glp_set_row_name(lp, 1, "p") glp.glp_set_row_bnds(lp, 1, glp.GLP_UP, 0.0, 100.0) glp.glp_set_row_name(lp, 2, "q") glp.glp_set_row_bnds(lp, 2, glp.GLP_UP, 0.0, 600.0) glp.glp_set_row_name(lp, 3, "r") glp.glp_set_row_bnds(lp, 3, glp.GLP_UP, 0.0, 300.0) glp.glp_add_cols(lp, 3) glp.glp_set_col_name(lp, 1, "x1") glp.glp_set_col_bnds(lp, 1, glp.GLP_LO, 0.0, 0.0) glp.glp_set_obj_coef(lp, 1, 10.0) glp.glp_set_col_name(lp, 2, "x2") glp.glp_set_col_bnds(lp, 2, glp.GLP_LO, 0.0, 0.0) glp.glp_set_obj_coef(lp, 2, 6.0) glp.glp_set_col_name(lp, 3, "x3") glp.glp_set_col_bnds(lp, 3, glp.GLP_LO, 0.0, 0.0) glp.glp_set_obj_coef(lp, 3, 4.0) ia[1] = 1; ja[1] = 1; ar[1] = 1.0 # a[1,1] = 1 ia[2] = 1; ja[2] = 2; ar[2] = 1.0 # a[1,2] = 1 ia[3] = 1; ja[3] = 3; ar[3] = 1.0 # a[1,3] = 1 ia[4] = 2; ja[4] = 1; ar[4] = 10.0 # a[2,1] = 10 ia[5] = 3; ja[5] = 1; ar[5] = 2.0 # a[3,1] = 2 ia[6] = 2; ja[6] = 2; ar[6] = 4.0 # a[2,2] = 4 ia[7] = 3; ja[7] = 2; ar[7] = 2.0 # a[3,2] = 2 ia[8] = 2; ja[8] = 3; ar[8] = 5.0 # a[2,3] = 5 ia[9] = 3; ja[9] = 3; ar[9] = 6.0 # a[3,3] = 6 glp.glp_load_matrix(lp, 9, ia, ja, ar) glp.glp_simplex(lp, smcp) Z = glp.glp_get_obj_val(lp) x1 = glp.glp_get_col_prim(lp, 1) x2 = glp.glp_get_col_prim(lp, 2) x3 = glp.glp_get_col_prim(lp, 3) self.assertAlmostEqual(Z, 733.3333, 4) self.assertAlmostEqual(x1, 33.3333, 4) self.assertAlmostEqual(x2, 66.6667, 4) self.assertAlmostEqual(x3, 0) glp.glp_delete_prob(lp)
def __del__(self):
try:
import swiglpk as glpk
except ImportError:
# Happens when python is shutting down. In this case, no garbage
# collection is necessary, anyway.
return
if self.int is not None:
glpk.glp_delete_prob(self.int)
def _linprog(c, A, b, obj):
lp = glpk.glp_create_prob()
glpk.glp_set_obj_dir(lp, obj)
params = glpk.glp_smcp()
glpk.glp_init_smcp(params)
params.msg_lev = glpk.GLP_MSG_OFF #Only print error messages from GLPK
num_rows = A.shape[0]
num_cols = A.shape[1]
mat_size = num_rows * num_cols
glpk.glp_add_rows(lp, num_rows)
for row_ind in range(num_rows):
glpk.glp_set_row_bnds(lp, row_ind + 1, glpk.GLP_UP, 0.0,
float(b[row_ind]))
glpk.glp_add_cols(lp, num_cols)
for col_ind in range(num_cols):
glpk.glp_set_col_bnds(lp, col_ind + 1, glpk.GLP_FR, 0.0, 0.0)
glpk.glp_set_obj_coef(lp, col_ind + 1, c[col_ind])
'Swig arrays are used for feeding constraints in GLPK'
ia, ja, ar = [], [], []
for i, j in product(range(num_rows), range(num_cols)):
ia.append(i + 1)
ja.append(j + 1)
ar.append(float(A[i][j]))
ia = glpk.as_intArray(ia)
ja = glpk.as_intArray(ja)
ar = glpk.as_doubleArray(ar)
glpk.glp_load_matrix(lp, mat_size, ia, ja, ar)
glpk.glp_simplex(lp, params)
fun = glpk.glp_get_obj_val(lp)
x = [
i for i in map(lambda x: glpk.glp_get_col_prim(lp, x + 1),
range(num_cols))
]
glpk.glp_delete_prob(lp)
glpk.glp_free_env()
return LPSolution(x, fun)
def serialize(self):
'serialize self.lp from a glpk instance into a tuple'
Timers.tic('serialize')
# get constraints as csr matrix
lp_rows = self.get_num_rows()
lp_cols = self.get_num_cols()
inds_row = SwigArray.get_int_array(lp_cols + 1)
vals_row = SwigArray.get_double_array(lp_cols + 1)
data = []
glpk_indices = []
indptr = [0]
for row in range(lp_rows):
got_len = glpk.glp_get_mat_row(self.lp, row + 1, inds_row,
vals_row)
for i in range(1, got_len + 1):
data.append(vals_row[i])
glpk_indices.append(inds_row[i])
indptr.append(len(data))
# rhs
rhs = []
for row in range(lp_rows):
assert glpk.glp_get_row_type(self.lp, row + 1) == glpk.GLP_UP
rhs.append(glpk.glp_get_row_ub(self.lp, row + 1))
col_bounds = self.get_col_bounds()
# remember to free lp object before overwriting with tuple
glpk.glp_delete_prob(self.lp)
self.lp = (data, glpk_indices, indptr, rhs, col_bounds)
Timers.toc('serialize')
def solve_IP(self):
#clear solution file
open("lp.sol", "w").close()
#create the LP
lp = glpk.glp_create_prob()
#create the model translator
tran = glpk.glp_mpl_alloc_wksp()
#read the model intro translator
glpk.glp_mpl_read_model(tran, "lp.mod", 0)
#generate the model
glpk.glp_mpl_generate(tran, None)
#build the LP from the model
glpk.glp_mpl_build_prob(tran, lp)
#create and init params for MIP solver
params = glpk.glp_iocp()
glpk.glp_init_iocp(params)
params.presolve = glpk.GLP_ON
#solve the MIP
glpk.glp_intopt(lp, params)
#save solution
#glpk.glp_write_sol(lp,"lp2.sol")
glpk.glp_mpl_postsolve(tran, lp, glpk.GLP_MIP)
#free resources
glpk.glp_mpl_free_wksp(tran)
glpk.glp_delete_prob(lp)
#read solution from model
self.read_solution()
#delete model and solution files
os.remove("lp.mod")
os.remove("lp.sol")
def reset_problem(self):
import swiglpk as glpk
if self.int is not None:
glpk.glp_delete_prob(self.int)
self.int = None
def solve(nutrition_target, foods):
'''
Calculate food amounts to reach the nutrition target
Parameters
----------
nutrition_target : soylent_recipes.nutrition_target.NormalizedNutritionTarget
The desired nutrition
foods : np.array
The foods to use to achieve the nutrition target. Contains exactly the
nutrients required by the nutrition target in the exact same order. Rows
represent foods, columns represent nutrients.
Returns
-------
amounts : np.array(int) or None
The amounts of each food to use to optimally achieve the nutrition
target. ``amounts[i]`` is the amount of the i-th food to use. If the
nutrition target cannot be achieved, returns None.
'''
# Implementation: using the GLPK C library via ecyglpki Python library binding
# GLPK documentation: download it and look inside the package (http://ftp.gnu.org/gnu/glpk/)
# GLPK wikibook: https://en.wikibooks.org/wiki/GLPK
#
# GPLK lingo: rows and columns refer to Ax=b where b_i are auxiliary
# variables, x_i are structural variables. Setting constraints on rows, set
# constraints on b_i, while column constraints are applied to x_i.
# Note: glpk is powerful. We're using mostly the default settings.
# Performance likely can be improved by tinkering with the settings; or even
# by providing the solution to the least squares equivalent, with amounts
# rounded afterwards, as starting point could improve performance.
nutrition_target = nutrition_target.values
problem = glp.glp_create_prob()
try:
glp.glp_add_rows(problem, len(nutrition_target))
glp.glp_add_cols(problem, len(foods))
# Configure columns/amounts
for i in range(len(foods)):
glp.glp_set_col_kind(problem, i+1, glp.GLP_IV) # int
glp.glp_set_col_bnds(problem, i+1, glp.GLP_LO, 0.0, np.nan) # >=0
# Configure rows/nutrients
for i, extrema in enumerate(nutrition_target):
if np.isnan(extrema[0]):
bounds_type = glp.GLP_UP
elif np.isnan(extrema[1]):
bounds_type = glp.GLP_LO
else:
# Note: a nutrition target has either min, max or both and min!=max
bounds_type = glp.GLP_DB
glp.glp_set_row_bnds(problem, i+1, bounds_type, *extrema)
# Load A of our Ax=b
non_zero_count = foods.size
row_indices = glp.intArray(non_zero_count+1) # +1 because (insane) 1-indexing
column_indices = glp.intArray(non_zero_count+1)
values = glp.doubleArray(non_zero_count+1)
for i, ((row, column), value) in enumerate(np.ndenumerate(foods.transpose())):
row_indices[i+1] = row+1
column_indices[i+1] = column+1
values[i+1] = value
glp.glp_load_matrix(problem, non_zero_count, row_indices, column_indices, values)
# Solve
int_opt_args = glp.glp_iocp()
glp.glp_init_iocp(int_opt_args)
int_opt_args.presolve = glp.GLP_ON # without this, you have to provide an LP relaxation basis
int_opt_args.msg_lev = glp.GLP_MSG_OFF # be quiet, no stdout
glp.glp_intopt(problem, int_opt_args) # returns an error code; can safely ignore
# Check we've got a valid solution
#
# Note: glp_intopt returns whether the algorithm completed successfully.
# This does not imply you've got a good solution, it could even be
# infeasible. glp_mip_status returns whether the solution is optimal,
# feasible, infeasible or undefined. An optimal/feasible solution is not
# necessarily a good solution. An optimal solution may even violate
# bounds constraints. The thing you actually need to use is
# glp_check_kkt and check that the solution satisfies KKT.PB (all within
# bounds)
max_error = glp.doubleArray(1)
glp.glp_check_kkt(problem, glp.GLP_MIP, glp.GLP_KKT_PB, max_error, None, None, None)
if not np.isclose(max_error[0], 0.0):
# A row/column value exceeds its bounds
return None
# Return solution
amounts = np.fromiter((glp.glp_mip_col_val(problem, i+1) for i in range(len(foods))), int)
return amounts
finally:
glp.glp_delete_prob(problem)
def __del__(self):
swiglpk.glp_delete_prob(self._p)
def __del__(self):
glp.glp_delete_prob(self._lp)
def __del__(self):
swiglpk.glp_delete_prob(self._p)
def delete_problem(lp):
glpk.glp_delete_prob(lp)
def __del__(self):
if hasattr(self, 'lp') and self.lp is not None:
glpk.glp_delete_prob(self.lp)
self.lp = None
