def build_sparse_linear_model_and_solve(cls, nb_vars, var_lbs, var_ubs,
var_types, var_names,
nb_rows, cts_sparse_coefs,
objsense, costs,
solution_maker=make_solution,
**transform_params):
cpx = cls.create_cplex()
# varlist = mdl.continuous_var_list(var_count, lb=var_lbs, ub=var_ubs, name=var_names)
cls.create_column_vars(cpx, nb_vars, var_lbs, var_ubs, var_types, var_names)
var_indices = list(range(nb_vars))
cpx_linexprs = [([], []) for _ in range(nb_rows)]
cpx_rhss = [0.] * nb_rows
for coef, row, col in cts_sparse_coefs:
if col >= nb_vars:
cpx_rhss[row] = float(coef)
elif coef:
cpx_row = cpx_linexprs[row]
# int() conversio nis mandatory here
# as sparse matrices contain numpy int types -> cause cplex to crash
cpx_row[0].append(int(col))
cpx_row[1].append(float(coef))
ctsense = ComparisonType.parse(transform_params.get('sense', 'le'))
cpx_senses = ctsense.cplex_code * nb_rows
#fast_add_linear(cpx, cpx_linexprs, cpx_senses, cpx_rhss, names=[])
cpx.linear_constraints.add(cpx_linexprs, cpx_senses, cpx_rhss, names=[])
if costs is not None:
# set linear objective for all variables.
fcosts = [float(k) for k in costs]
static_fast_set_linear_obj(cpx, var_indices, fcosts)
cpx.objective.set_sense(objsense.cplex_coef)
# here we go to solve...
return cls._solve(cpx, var_names, solution_maker=solution_maker, **transform_params)
def build_matrix_range_model_and_solve(cls, var_count, var_lbs, var_ubs,
var_types, var_names,
cts_mat, range_mins, range_maxs,
objsense, costs,
cast_to_float,
solution_maker=make_solution,
**transform_params):
cpx = cls.create_cplex()
# varlist = mdl.continuous_var_list(var_count, lb=var_lbs, ub=var_ubs, name=var_names)
cls.create_column_vars(cpx, var_count, var_lbs, var_ubs, var_types, var_names)
var_indices = list(range(var_count))
gen_rows = ModelAggregator.generate_rows(cts_mat)
cpx_rows = []
for row in gen_rows:
# need this step as cplex may crash with np types.
frow = [float(k) for k in row] if cast_to_float else row
cpx_rows.append([var_indices, frow])
nb_rows = len(cpx_rows)
if nb_rows:
#ctsense = ComparisonType.parse(transform_params.get('sense', 'le'))
cpx_senses = 'R' * nb_rows
cpx_ranges = [float(rmin - rmax) for rmin, rmax in izip(range_mins, range_maxs)]
# rhs is UB
cpx_rhss = [float(rmax) for rmax in range_maxs]
add_linear(cpx, cpx_rows, cpx_senses, rhs=cpx_rhss, names=[], ranges=cpx_ranges)
if costs is not None:
# set linear objective for all variables.
fcosts = [float(k) for k in costs]
static_fast_set_linear_obj(cpx, var_indices, fcosts)
cpx.objective.set_sense(objsense.cplex_coef)
# here we go to solve...
return cls._solve(cpx, var_names, solution_maker=solution_maker, **transform_params)
def build_matrix_linear_model_and_solve(cls,
var_count,
var_lbs,
var_ubs,
var_types,
var_names,
cts_mat,
rhs,
objsense,
costs,
cast_to_float,
solution_maker=make_solution,
**transform_params):
cpx = cls.create_cplex()
if cast_to_float:
print("-- all numbers will be cast to float")
else:
print("-- no cast to float is performed")
cls.create_column_vars(cpx, var_count, var_lbs, var_ubs, var_types,
var_names)
var_indices = list(range(var_count))
gen_rows = ModelAggregator.generate_rows(cts_mat)
cpx_rows = []
if cast_to_float:
for row in gen_rows:
# need this step as cplex may crash with np types.
frow = [float(k) for k in row]
cpx_rows.append([var_indices, frow])
else:
cpx_rows = [[var_indices, row] for row in gen_rows]
nb_rows = len(cpx_rows)
if nb_rows:
ctsense = ComparisonType.parse(transform_params.get('sense', 'le'))
cpx_senses = ctsense.cplex_code * nb_rows
cpx_rhss = [float(r) for r in rhs] if cast_to_float else rhs
fast_add_linear(cpx, cpx_rows, cpx_senses, cpx_rhss, names=[])
if costs is not None:
# set linear objective for all variables.
fcosts = [float(k) for k in costs]
static_fast_set_linear_obj(cpx, var_indices, fcosts)
cpx.objective.set_sense(objsense.cplex_coef)
# here we go to solve...
return cls._solve(cpx,
var_names,
solution_maker=solution_maker,
**transform_params)