def __init__(self, **kwargs): self._p = swiglpk.glp_create_prob() self._variables = {} self._constraints = {} self._result = None
def __init__(self, quadratic_objective=False): if quadratic_objective: raise Exception('Quadratic objective not supported for GLPK') self._lp = glp.glp_create_prob() self._smcp = glp.glp_smcp() # simplex solver control parameters glp.glp_init_smcp(self._smcp) self._smcp.msg_lev = glp.GLP_MSG_ERR self.simplex_iteration_limit = 10000 self._n_vars = 0 self._n_eq_constraints = 0 self._flows = {} self._lb = {} self._ub = {} self._objective = {} self._materialCoeffs = defaultdict(list) self._materialIdxLookup = {} self._eqConstBuilt = False self._solved = False self.inf = np.inf self._lowerBoundDefault = 0 self._upperBoundDefault = self.inf
def _initialize_problem(self): self.problem = glp_create_prob() glp_create_index(self.problem) glp_scale_prob(self.problem, GLP_SF_AUTO) if self.name is not None: _glpk_validate_id(self.name) glp_set_prob_name(self.problem, str(self.name))
def __setstate__(self, repr_dict): with TemporaryFilename(suffix=".glpk", content=repr_dict["glpk_repr"]) as tmp_file_name: problem = glp_create_prob() glp_read_prob(problem, 0, tmp_file_name) self.__init__(problem=problem) self.configuration = Configuration.clone(repr_dict['config'], problem=self) if repr_dict['glpk_status'] == 'optimal': self.optimize() # since the start is an optimal solution, nothing will happen here
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 __setstate__(self, repr_dict): tmp_file = tempfile.mktemp(suffix=".glpk") open(tmp_file, 'w').write(repr_dict['glpk_repr']) problem = glp_create_prob() glp_read_prob(problem, 0, tmp_file) self.__init__(problem=problem) self.configuration = Configuration.clone(repr_dict['config'], problem=self) if repr_dict['glpk_status'] == 'optimal': self.optimize() # since the start is an optimal solution, nothing will happen here
def read_netlib_sif_glpk(fhandle): tmp_file = tempfile.mktemp(suffix='.mps') with open(tmp_file, 'w') as tmp_handle: content = ''.join([str(s) for s in fhandle if str(s.strip())]) tmp_handle.write(content) fhandle.close() problem = glp_create_prob() glp_read_mps(problem, GLP_MPS_DECK, None, tmp_file) # glp_read_mps(problem, GLP_MPS_FILE, None, tmp_file) return problem
def __setstate__(self, repr_dict): with TemporaryFilename( suffix=".glpk", content=repr_dict["glpk_repr"]) as tmp_file_name: problem = glp_create_prob() glp_read_prob(problem, 0, tmp_file_name) self.__init__(problem=problem) self.configuration = Configuration.clone(repr_dict['config'], problem=self) if repr_dict['glpk_status'] == 'optimal': self.optimize( ) # since the start is an optimal solution, nothing will happen here
def __setstate__(self, repr_dict): with TemporaryFilename(suffix=".glpk", content=repr_dict["glpk_repr"]) as tmp_file_name: problem = glp_create_prob() code = glp_read_prob(problem, 0, tmp_file_name) if code != 0: with open(tmp_file_name) as tmp_file: invalid_problem = tmp_file.read() raise Exception("The GLPK file " + tmp_file_name + " does not seem to contain a valid GLPK problem:\n\n" + invalid_problem) self.__init__(problem=problem) self.configuration = Configuration.clone(repr_dict['config'], problem=self) if repr_dict['glpk_status'] == 'optimal': self.optimize() # since the start is an optimal solution, nothing will happen here
def glpk_read_cplex(path): """Reads cplex file and returns glpk problem. Returns ------- glp_prob A glpk problems (same type as returned by glp_create_prob) """ from swiglpk import glp_create_prob, glp_read_lp problem = glp_create_prob() glp_read_lp(problem, None, path) return problem
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 __setstate__(self, repr_dict): with tempfile.NamedTemporaryFile(suffix=".glpk", delete=True) as tmp_file: tmp_file_name = tmp_file.name with open(tmp_file_name, 'w') as tmp_file: tmp_file.write(repr_dict['glpk_repr']) problem = glp_create_prob() glp_read_prob(problem, 0, tmp_file_name) self.__init__(problem=problem) self.configuration = Configuration.clone(repr_dict['config'], problem=self) if repr_dict['glpk_status'] == 'optimal': self.optimize( ) # since the start is an optimal solution, nothing will happen here
def load_problem(mps_file): prob_tmp_file = tempfile.mktemp(suffix='.mps') with open(prob_tmp_file, 'wb') as tmp_handle: f = gzip.open(mps_file, 'rb') tmp_handle.write(f.read()) f.close() problem = glp_create_prob() glp_read_mps(problem, GLP_MPS_FILE, None, prob_tmp_file) model = Model(problem=problem) model.configuration.presolve = True model.configuration.verbosity = 3 model.configuration.timeout = 60 * 9 return problem, model
def __init__(self): 'initialize the lp instance' self.lp = glpk.glp_create_prob() # pylint: disable=invalid-name # these are assigned on set_reach_vars() self.dims = None self.basis_mat_rect = None # 4-tuple, x, y, w, h self.cur_vars_offset = None # internal bookkeeping self.obj_cols = [ ] # columns in the LP with an assigned objective coefficient self.names = [] # column names self.freeze_attrs()
def __init__(self): 'initialize the lp instance' self.lp = glpk.glp_create_prob() # pylint: disable=invalid-name # these are assigned on set_reach_vars() self.dims = None self.basis_mat_pos = None # 2-tuple, row, column (NOT X/Y) self.cur_vars_offset = None self.input_effects_offsets = None # None or 2-tuple, row of input constraints / col of accumulated input effects # internal bookkeeping self.obj_cols = [] # columns in the LP with an assigned objective coefficient self.names = [] # column names self.bm_indices = None # a list of intArray for each row, assigned on set_reach_vars self.freeze_attrs()
def __init__(self, other_lpi=None): 'initialize the lp instance' self.lp = glpk.glp_create_prob() # pylint: disable=invalid-name if other_lpi is None: # internal bookkeeping self.names = [] # column names # setup lp params else: # initialize from other lpi self.names = other_lpi.names.copy() Timers.tic('glp_copy_prob') glpk.glp_copy_prob(self.lp, other_lpi.lp, glpk.GLP_OFF) Timers.toc('glp_copy_prob') self.freeze_attrs()
def setup_linprog( recipes: Dict[str, 'Recipe'], min_rates: Optional[Dict[str, float]] = None, min_clocks: Optional[Dict[str, int]] = None, fixed_clocks: Optional[Dict[str, int]] = None, ) -> SwigPyObject: if min_rates is None: min_rates = {} if min_clocks is None: min_clocks = {} if fixed_clocks is None: fixed_clocks = {} resources = sorted({p for r in recipes.values() for p in r.rates.keys()}) resource_indices: Dict[str, int] = {r: i for i, r in enumerate(resources, 1)} m = len(resources) n = len(recipes) problem: SwigPyObject = lp.glp_create_prob() lp.glp_set_prob_name(problem, 'satisfactory') lp.glp_set_obj_name(problem, 'percentage_sum') lp.glp_set_obj_dir(problem, lp.GLP_MIN) lp.glp_add_rows(problem, m) lp.glp_add_cols(problem, n) for i, resource in enumerate(resources, 1): setup_row(problem, i, resource, min_rates.get(resource, 0)) for j, recipe in enumerate(recipes.values(), 1): setup_col( problem, j, recipe, resource_indices, min_clocks.get(recipe.name), fixed_clocks.get(recipe.name), ) lp.glp_create_index(problem) return problem
def __init__(self, **kwargs): self._p = swiglpk.glp_create_prob() self._variables = {} self._constraints = {} self._do_presolve = True # Initialize simplex tolerances to default values parm = swiglpk.glp_smcp() swiglpk.glp_init_smcp(parm) self._feasibility_tolerance = parm.tol_bnd self._optimality_tolerance = parm.tol_dj # Initialize mip tolerance to default value parm = swiglpk.glp_iocp() swiglpk.glp_init_iocp(parm) self._integrality_tolerance = parm.tol_int self._result = None
def deserialize(self): 'deserialize self.lp from a tuple into a glpk_instance' assert isinstance(self.lp, tuple) Timers.tic('deserialize') data, glpk_indices, indptr, rhs, col_bounds = self.lp self.lp = glpk.glp_create_prob() # add cols names = self.names self.names = [] # adding columns populates self.names num_cols = len(col_bounds) for i, (lb, ub) in enumerate(col_bounds): name = names[i] if ub == np.inf: if lb == -np.inf: # free variable self.add_cols([name]) else: assert lb == 0 self.add_positive_cols([name]) else: self.add_double_bounded_cols([name], lb, ub) # add rows num_rows = len(rhs) self.add_rows_less_equal(rhs) # set constraints shape = (num_rows, num_cols) self.set_constraints_csr(data, glpk_indices, indptr, shape) Timers.toc('deserialize')
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 from_lp(cls, lp_form): problem = glp_create_prob() with TemporaryFilename(suffix=".lp", content=lp_form) as tmp_file_name: glp_read_lp(problem, None, tmp_file_name) model = cls(problem=problem) return model
def create_minimization_problem(): glpk.glp_term_out(glpk.GLP_OFF) lp = glpk.glp_create_prob() glpk.glp_set_obj_dir(lp, glpk.GLP_MIN) return lp
c2 = Constraint(10 * x1 + 4 * x2 + 5 * x3, ub=600, name='c2') c3 = Constraint(2 * x1 + 2 * x2 + 6 * x3, ub=300, name='c3') obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max') model = Model(name='Simple model') model.objective = obj model.add([c1, c2, c3]) status = model.optimize() print("status:", model.status) print("objective value:", model.objective.value) for var_name, var in model.variables.items(): print(var_name, "=", var.primal) print(model) problem = glp_create_prob() glp_read_lp(problem, None, "tests/data/model.lp") solver = Model(problem=problem) print(solver.optimize()) print(solver.objective) import time t1 = time.time() print("pickling") pickle_string = pickle.dumps(solver) resurrected_solver = pickle.loads(pickle_string) t2 = time.time() print("Execution time: %s" % (t2 - t1))
def test_solve_with_glpsol(): problem = glp_create_prob() glp_read_lp(problem, None, TESTMODELPATH) glp_create_index(problem) result = solve_with_glpsol(problem) reference = { 'M_13dpg_c': ['5', '0', '-0.0471054946649844'], 'R_ME1': ['2', '0', '-0.00509248590972805'], 'R_ME2': ['2', '0', '-0.00381936443229605'], 'R_EX_acald_e': ['2', '0', '-0.0343742798906643'], 'R_EX_pi_e': ['1', '-3.21489504768477', '0'], 'R_PTAr': ['1', '0', '0'], 'M_g6p_c': ['5', '0', '-0.0980303537622649'], 'R_THD2': ['2', '0', '-0.00127312147743202'], 'M_s7p_c': ['5', '0', '-0.113307811491449'], 'M_glu_L_c': ['5', '0', '-0.0700216812587607'], 'R_TKT1': ['1', '1.49698375726157', '0'], 'M_glu_L_e': ['5', '0', '-0.0687485597813287'], 'R_CO2t': ['1', '-22.809833310205', '0'], 'R_PFL': ['1', '5.31182206840464e-31', '0'], 'M_nadp_c': ['5', '0', '0.00891185034202407'], 'R_ICL': ['1', '0', '0'], 'M_h2o_c': ['5', '0', '-0'], 'M_amp_c': ['5', '0', '0.0101849718194561'], 'M_f6p_c': ['5', '0', '-0.0980303537622649'], 'M_h2o_e': ['5', '0', '-0'], 'M_ac_c': ['5', '0', '-0.0241893080712082'], 'M_ac_e': ['5', '0', '-0.0229161865937762'], 'R_NH4t': ['1', '4.76531919319746', '0'], 'M_adp_c': ['5', '0', '0.00509248590972805'], 'M_nh4_e': ['5', '0', '-0'], 'M_gln_L_c': ['5', '0', '-0.0751141671684887'], 'M_gln_L_e': ['5', '0', '-0.0700216812587607'], 'M_succoa_c': ['5', '0', '-0.0547442235295765'], 'M_nh4_c': ['5', '0', '-0'], 'R_H2Ot': ['1', '-29.1758271355658', '0'], 'R_EX_glu_L_e': ['2', '0', '-0.0687485597813287'], 'M_icit_c': ['5', '0', '-0.0712948027361927'], 'M_q8h2_c': ['1', '-7.105427357601e-15', '0'], 'R_FORt2': ['2', '0', '-0.00127312147743201'], 'R_ADK1': ['1', '0', '0'], 'M_glx_c': ['5', '0', '-0.0203699436389122'], 'M_glc_D_e': ['5', '0', '-0.0916647463751049'], 'M_lac_D_c': ['5', '0', '-0.0420130087552564'], 'M_lac_D_e': ['5', '0', '-0.0407398872778244'], 'M_acald_e': ['5', '0', '-0.0343742798906643'], 'M_acald_c': ['5', '0', '-0.0343742798906643'], 'R_RPE': ['1', '2.67848185050753', '0'], 'R_EX_for_e': ['2', '0', '-0.00763872886459208'], 'M_dhap_c': ['5', '0', '-0.0521979805747125'], 'M_pyr_c': ['5', '0', '-0.0356474013680963'], 'M_pyr_e': ['5', '0', '-0.0343742798906643'], 'M_fdp_c': ['5', '0', '-0.104395961149425'], 'R_PGM': ['1', '-14.7161395687428', '0'], 'R_PGL': ['1', '4.95998494457466', '0'], 'R_PGK': ['1', '-16.0235261431676', '0'], 'M_3pg_c': ['5', '0', '-0.0420130087552564'], 'M_coa_c': ['1', '-1.77635683940025e-15', '0'], 'R_FRD7': ['2', '0', '0'], 'R_EX_gln_L_e': ['2', '0', '-0.0700216812587607'], 'R_GLCpts': ['1', '10', '0'], 'R_SUCCt3': ['1', '0', '0'], 'R_ATPM': ['2', '8.39', '-0.00509248590972805'], 'M_succ_e': ['5', '0', '-0.0521979805747125'], 'M_succ_c': ['5', '0', '-0.0509248590972805'], 'R_EX_lac_D_e': ['2', '0', '-0.0407398872778244'], 'R_O2t': ['1', '21.7994926559988', '0'], 'M_2pg_c': ['5', '0', '-0.0420130087552564'], 'R_CS': ['1', '6.00724957535033', '0'], 'R_PDH': ['1', '9.28253259916661', '0'], 'R_CYTBD': ['1', '43.5989853119975', '0'], 'R_ETOHt2r': ['1', '-0', '0'], 'R_FBP': ['2', '0', '-0.00509248590972805'], 'R_ACKr': ['1', '0', '0'], 'R_GLUSy': ['2', '0', '-0.00509248590972804'], 'M_fru_e': ['5', '0', '-0.0916647463751049'], 'R_G6PDH2r': ['1', '4.95998494457466', '0'], 'R_EX_co2_e': ['1', '22.809833310205', '0'], 'R_TKT2': ['1', '1.18149809324596', '0'], 'R_GLUDy': ['1', '-4.54185746386563', '0'], 'R_EX_fru_e': ['2', '0', '-0.0916647463751049'], 'R_NADTRHD': ['2', '0', '-0.001273121477432'], 'R_PYRt2r': ['1', '-0', '0'], 'R_FUMt2_2': ['1', '0', '0'], 'R_SUCDi': ['1', '5.06437566148209', '0'], 'R_ALCD2x': ['1', '-5.84859702315521e-30', '0'], 'R_EX_o2_e': ['1', '-21.7994926559988', '0'], 'M_g3p_c': ['5', '0', '-0.0521979805747125'], 'R_EX_akg_e': ['2', '0', '-0.0611098309167366'], 'R_GLUt2r': ['1', '-0', '0'], 'M_pi_c': ['5', '0', '-0.00127312147743201'], 'M_pi_e': ['5', '0', '-0'], 'R_LDH_D': ['1', '0', '0'], 'M_o2_c': ['5', '0', '-0'], 'M_atp_c': ['1', '0', '0'], 'M_o2_e': ['5', '0', '-0'], 'R_MALt2_2': ['1', '0', '0'], 'R_FBA': ['1', '7.47738196216028', '0'], 'M_for_c': ['5', '0', '-0.00763872886459208'], 'R_EX_pyr_e': ['2', '0', '-0.0343742798906643'], 'R_EX_h_e': ['1', '17.5308654297867', '0'], 'R_MALS': ['2', '0', '-0.001273121477432'], 'M_h_c': ['5', '0', '0.00127312147743201'], 'M_h_e': ['5', '0', '-0'], 'R_TALA': ['1', '1.49698375726157', '0'], 'R_SUCOAS': ['1', '-5.06437566148209', '0'], 'M_pep_c': ['5', '0', '-0.0420130087552564'], 'R_ICDHyr': ['1', '6.00724957535033', '0'], 'R_RPI': ['1', '-2.28150309406713', '0'], 'M_accoa_c': ['5', '0', '-0.0280086725035043'], 'R_EX_ac_e': ['2', '0', '-0.0229161865937762'], 'M_6pgl_c': ['5', '0', '-0.0903916248976729'], 'R_PFK': ['1', '7.47738196216028', '0'], 'M_oaa_c': ['5', '0', '-0.0420130087552564'], 'R_EX_glc_e': ['2', '-10', '-0.0916647463751049'], 'R_EX_h2o_e': ['1', '29.1758271355658', '0'], 'M_mal_L_c': ['5', '0', '-0.0483786161424165'], 'R_EX_nh4_e': ['1', '-4.76531919319746', '0'], 'M_acon_C_c': ['5', '0', '-0.0712948027361927'], 'R_ACALDt': ['1', '0', '0'], 'R_GLNS': ['1', '0.223461729331828', '0'], 'M_r5p_c': ['5', '0', '-0.0827528960330808'], 'R_ACONTb': ['1', '6.00724957535033', '0'], 'M_actp_c': ['5', '0', '-0.0292817939809363'], 'M_cit_c': ['5', '0', '-0.0712948027361927'], 'M_mal_L_e': ['5', '0', '-0.0458323731875524'], 'M_akg_c': ['5', '0', '-0.0623829523941686'], 'M_akg_e': ['5', '0', '-0.0611098309167366'], 'R_D_LACt2': ['1', '-0', '0'], 'R_ATPS4r': ['1', '45.5140097745175', '0'], 'M_ru5p_D_c': ['5', '0', '-0.0827528960330808'], 'R_TPI': ['1', '7.47738196216028', '0'], 'R_PPCK': ['2', '0', '-0.00509248590972805'], 'R_SUCCt2_2': ['2', '0', '-0.00381936443229604'], 'M_e4p_c': ['5', '0', '-0.0674754383038966'], 'R_NADH16': ['1', '38.5346096505154', '0'], 'R_Biomass_Ecoli_core_w_GAM': ['1', '0.87392150696843', '0'], 'R_GAPD': ['1', '16.0235261431676', '0'], 'R_PGI': ['1', '4.86086114649682', '0'], 'R_GLNabc': ['1', '0', '0'], 'R_AKGDH': ['1', '5.06437566148209', '0'], 'R_MDH': ['1', '5.06437566148209', '0'], 'R_EX_fum_e': ['2', '0', '-0.0458323731875524'], 'R_PYK': ['1', '1.75817744410678', '0'], 'M_etoh_c': ['5', '0', '-0.0407398872778244'], 'M_fum_c': ['5', '0', '-0.0483786161424165'], 'M_q8_c': ['5', '0', '0.00254624295486403'], 'M_etoh_e': ['5', '0', '-0.0394667658003924'], 'M_fum_e': ['5', '0', '-0.0458323731875524'], 'R_FRUpts2': ['1', '0', '0'], 'M_nadph_c': ['1', '-8.88178419700125e-16', '0'], 'R_ENO': ['1', '14.7161395687428', '0'], 'R_PIt2r': ['1', '3.21489504768477', '0'], 'R_EX_mal_L_e': ['2', '0', '-0.0458323731875524'], 'R_ACALD': ['1', '-5.84859702315521e-30', '0'], 'M_for_e': ['5', '0', '-0.00763872886459208'], 'M_nad_c': ['5', '0', '0.00763872886459208'], 'M_6pgc_c': ['5', '0', '-0.0916647463751049'], 'R_FORti': ['1', '5.31182206840464e-31', '0'], 'M_co2_c': ['5', '0', '-0'], 'R_PPS': ['2', '0', '-0.00509248590972805'], 'M_co2_e': ['5', '0', '-0'], 'R_EX_succ_e': ['2', '0', '-0.0521979805747125'], 'R_ACONTa': ['1', '6.00724957535033', '0'], 'M_nadh_c': ['1', '5.32907051820075e-15', '0'], 'R_FUM': ['1', '5.06437566148209', '0'], 'R_GND': ['1', '4.95998494457465', '0'], 'R_ACt2r': ['1', '-0', '0'], 'R_PPC': ['1', '2.50430947036873', '0'], 'R_EX_etoh_e': ['2', '0', '-0.0394667658003924'], 'R_AKGt2r': ['1', '-0', '0'], 'R_GLUN': ['2', '0', '-0.00509248590972805'] } for key, val in six.iteritems(result): assert val == reference[key]
def __init__(self, problem=None, *args, **kwargs): super(Model, self).__init__(*args, **kwargs) self.configuration = Configuration() if problem is None: self.problem = glp_create_prob() glp_create_index(self.problem) if self.name is not None: glp_set_prob_name(self.problem, str(self.name)) else: try: self.problem = problem glp_create_index(self.problem) except TypeError: raise TypeError("Provided problem is not a valid GLPK model.") row_num = glp_get_num_rows(self.problem) col_num = glp_get_num_cols(self.problem) for i in range(1, col_num + 1): var = Variable(glp_get_col_name(self.problem, i), lb=glp_get_col_lb(self.problem, i), ub=glp_get_col_ub(self.problem, i), problem=self, type=_GLPK_VTYPE_TO_VTYPE[glp_get_col_kind( self.problem, i)]) # This avoids adding the variable to the glpk problem super(Model, self)._add_variables([var]) variables = self.variables for j in range(1, row_num + 1): ia = intArray(col_num + 1) da = doubleArray(col_num + 1) nnz = glp_get_mat_row(self.problem, j, ia, da) constraint_variables = [ variables[ia[i] - 1] for i in range(1, nnz + 1) ] # Since constraint expressions are lazily retrieved from the solver they don't have to be built here # lhs = _unevaluated_Add(*[da[i] * constraint_variables[i - 1] # for i in range(1, nnz + 1)]) lhs = 0 glpk_row_type = glp_get_row_type(self.problem, j) if glpk_row_type == GLP_FX: row_lb = glp_get_row_lb(self.problem, j) row_ub = row_lb elif glpk_row_type == GLP_LO: row_lb = glp_get_row_lb(self.problem, j) row_ub = None elif glpk_row_type == GLP_UP: row_lb = None row_ub = glp_get_row_ub(self.problem, j) elif glpk_row_type == GLP_DB: row_lb = glp_get_row_lb(self.problem, j) row_ub = glp_get_row_ub(self.problem, j) elif glpk_row_type == GLP_FR: row_lb = None row_ub = None else: raise Exception( "Currently, optlang does not support glpk row type %s" % str(glpk_row_type)) log.exception() if isinstance(lhs, int): lhs = sympy.Integer(lhs) elif isinstance(lhs, float): lhs = sympy.RealNumber(lhs) constraint_id = glp_get_row_name(self.problem, j) for variable in constraint_variables: try: self._variables_to_constraints_mapping[ variable.name].add(constraint_id) except KeyError: self._variables_to_constraints_mapping[ variable.name] = set([constraint_id]) super(Model, self)._add_constraints([ Constraint(lhs, lb=row_lb, ub=row_ub, name=constraint_id, problem=self, sloppy=True) ], sloppy=True) term_generator = ((glp_get_obj_coef(self.problem, index), variables[index - 1]) for index in range(1, glp_get_num_cols(problem) + 1)) self._objective = Objective(_unevaluated_Add(*[ _unevaluated_Mul(sympy.RealNumber(term[0]), term[1]) for term in term_generator if term[0] != 0. ]), problem=self, direction={ GLP_MIN: 'min', GLP_MAX: 'max' }[glp_get_obj_dir(self.problem)]) glp_scale_prob(self.problem, GLP_SF_AUTO)
def test_solve_with_glpsol(): problem = glp_create_prob() glp_read_lp(problem, None, TESTMODELPATH) glp_create_index(problem) result = solve_with_glpsol(problem) reference = {'M_13dpg_c': ['5', '0', '-0.0471054946649844'], 'R_ME1': ['2', '0', '-0.00509248590972805'], 'R_ME2': ['2', '0', '-0.00381936443229605'], 'R_EX_acald_e': ['2', '0', '-0.0343742798906643'], 'R_EX_pi_e': ['1', '-3.21489504768477', '0'], 'R_PTAr': ['1', '0', '0'], 'M_g6p_c': ['5', '0', '-0.0980303537622649'], 'R_THD2': ['2', '0', '-0.00127312147743202'], 'M_s7p_c': ['5', '0', '-0.113307811491449'], 'M_glu_L_c': ['5', '0', '-0.0700216812587607'], 'R_TKT1': ['1', '1.49698375726157', '0'], 'M_glu_L_e': ['5', '0', '-0.0687485597813287'], 'R_CO2t': ['1', '-22.809833310205', '0'], 'R_PFL': ['1', '5.31182206840464e-31', '0'], 'M_nadp_c': ['5', '0', '0.00891185034202407'], 'R_ICL': ['1', '0', '0'], 'M_h2o_c': ['5', '0', '-0'], 'M_amp_c': ['5', '0', '0.0101849718194561'], 'M_f6p_c': ['5', '0', '-0.0980303537622649'], 'M_h2o_e': ['5', '0', '-0'], 'M_ac_c': ['5', '0', '-0.0241893080712082'], 'M_ac_e': ['5', '0', '-0.0229161865937762'], 'R_NH4t': ['1', '4.76531919319746', '0'], 'M_adp_c': ['5', '0', '0.00509248590972805'], 'M_nh4_e': ['5', '0', '-0'], 'M_gln_L_c': ['5', '0', '-0.0751141671684887'], 'M_gln_L_e': ['5', '0', '-0.0700216812587607'], 'M_succoa_c': ['5', '0', '-0.0547442235295765'], 'M_nh4_c': ['5', '0', '-0'], 'R_H2Ot': ['1', '-29.1758271355658', '0'], 'R_EX_glu_L_e': ['2', '0', '-0.0687485597813287'], 'M_icit_c': ['5', '0', '-0.0712948027361927'], 'M_q8h2_c': ['1', '-7.105427357601e-15', '0'], 'R_FORt2': ['2', '0', '-0.00127312147743201'], 'R_ADK1': ['1', '0', '0'], 'M_glx_c': ['5', '0', '-0.0203699436389122'], 'M_glc_D_e': ['5', '0', '-0.0916647463751049'], 'M_lac_D_c': ['5', '0', '-0.0420130087552564'], 'M_lac_D_e': ['5', '0', '-0.0407398872778244'], 'M_acald_e': ['5', '0', '-0.0343742798906643'], 'M_acald_c': ['5', '0', '-0.0343742798906643'], 'R_RPE': ['1', '2.67848185050753', '0'], 'R_EX_for_e': ['2', '0', '-0.00763872886459208'], 'M_dhap_c': ['5', '0', '-0.0521979805747125'], 'M_pyr_c': ['5', '0', '-0.0356474013680963'], 'M_pyr_e': ['5', '0', '-0.0343742798906643'], 'M_fdp_c': ['5', '0', '-0.104395961149425'], 'R_PGM': ['1', '-14.7161395687428', '0'], 'R_PGL': ['1', '4.95998494457466', '0'], 'R_PGK': ['1', '-16.0235261431676', '0'], 'M_3pg_c': ['5', '0', '-0.0420130087552564'], 'M_coa_c': ['1', '-1.77635683940025e-15', '0'], 'R_FRD7': ['2', '0', '0'], 'R_EX_gln_L_e': ['2', '0', '-0.0700216812587607'], 'R_GLCpts': ['1', '10', '0'], 'R_SUCCt3': ['1', '0', '0'], 'R_ATPM': ['2', '8.39', '-0.00509248590972805'], 'M_succ_e': ['5', '0', '-0.0521979805747125'], 'M_succ_c': ['5', '0', '-0.0509248590972805'], 'R_EX_lac_D_e': ['2', '0', '-0.0407398872778244'], 'R_O2t': ['1', '21.7994926559988', '0'], 'M_2pg_c': ['5', '0', '-0.0420130087552564'], 'R_CS': ['1', '6.00724957535033', '0'], 'R_PDH': ['1', '9.28253259916661', '0'], 'R_CYTBD': ['1', '43.5989853119975', '0'], 'R_ETOHt2r': ['1', '-0', '0'], 'R_FBP': ['2', '0', '-0.00509248590972805'], 'R_ACKr': ['1', '0', '0'], 'R_GLUSy': ['2', '0', '-0.00509248590972804'], 'M_fru_e': ['5', '0', '-0.0916647463751049'], 'R_G6PDH2r': ['1', '4.95998494457466', '0'], 'R_EX_co2_e': ['1', '22.809833310205', '0'], 'R_TKT2': ['1', '1.18149809324596', '0'], 'R_GLUDy': ['1', '-4.54185746386563', '0'], 'R_EX_fru_e': ['2', '0', '-0.0916647463751049'], 'R_NADTRHD': ['2', '0', '-0.001273121477432'], 'R_PYRt2r': ['1', '-0', '0'], 'R_FUMt2_2': ['1', '0', '0'], 'R_SUCDi': ['1', '5.06437566148209', '0'], 'R_ALCD2x': ['1', '-5.84859702315521e-30', '0'], 'R_EX_o2_e': ['1', '-21.7994926559988', '0'], 'M_g3p_c': ['5', '0', '-0.0521979805747125'], 'R_EX_akg_e': ['2', '0', '-0.0611098309167366'], 'R_GLUt2r': ['1', '-0', '0'], 'M_pi_c': ['5', '0', '-0.00127312147743201'], 'M_pi_e': ['5', '0', '-0'], 'R_LDH_D': ['1', '0', '0'], 'M_o2_c': ['5', '0', '-0'], 'M_atp_c': ['1', '0', '0'], 'M_o2_e': ['5', '0', '-0'], 'R_MALt2_2': ['1', '0', '0'], 'R_FBA': ['1', '7.47738196216028', '0'], 'M_for_c': ['5', '0', '-0.00763872886459208'], 'R_EX_pyr_e': ['2', '0', '-0.0343742798906643'], 'R_EX_h_e': ['1', '17.5308654297867', '0'], 'R_MALS': ['2', '0', '-0.001273121477432'], 'M_h_c': ['5', '0', '0.00127312147743201'], 'M_h_e': ['5', '0', '-0'], 'R_TALA': ['1', '1.49698375726157', '0'], 'R_SUCOAS': ['1', '-5.06437566148209', '0'], 'M_pep_c': ['5', '0', '-0.0420130087552564'], 'R_ICDHyr': ['1', '6.00724957535033', '0'], 'R_RPI': ['1', '-2.28150309406713', '0'], 'M_accoa_c': ['5', '0', '-0.0280086725035043'], 'R_EX_ac_e': ['2', '0', '-0.0229161865937762'], 'M_6pgl_c': ['5', '0', '-0.0903916248976729'], 'R_PFK': ['1', '7.47738196216028', '0'], 'M_oaa_c': ['5', '0', '-0.0420130087552564'], 'R_EX_glc_e': ['2', '-10', '-0.0916647463751049'], 'R_EX_h2o_e': ['1', '29.1758271355658', '0'], 'M_mal_L_c': ['5', '0', '-0.0483786161424165'], 'R_EX_nh4_e': ['1', '-4.76531919319746', '0'], 'M_acon_C_c': ['5', '0', '-0.0712948027361927'], 'R_ACALDt': ['1', '0', '0'], 'R_GLNS': ['1', '0.223461729331828', '0'], 'M_r5p_c': ['5', '0', '-0.0827528960330808'], 'R_ACONTb': ['1', '6.00724957535033', '0'], 'M_actp_c': ['5', '0', '-0.0292817939809363'], 'M_cit_c': ['5', '0', '-0.0712948027361927'], 'M_mal_L_e': ['5', '0', '-0.0458323731875524'], 'M_akg_c': ['5', '0', '-0.0623829523941686'], 'M_akg_e': ['5', '0', '-0.0611098309167366'], 'R_D_LACt2': ['1', '-0', '0'], 'R_ATPS4r': ['1', '45.5140097745175', '0'], 'M_ru5p_D_c': ['5', '0', '-0.0827528960330808'], 'R_TPI': ['1', '7.47738196216028', '0'], 'R_PPCK': ['2', '0', '-0.00509248590972805'], 'R_SUCCt2_2': ['2', '0', '-0.00381936443229604'], 'M_e4p_c': ['5', '0', '-0.0674754383038966'], 'R_NADH16': ['1', '38.5346096505154', '0'], 'R_Biomass_Ecoli_core_w_GAM': ['1', '0.87392150696843', '0'], 'R_GAPD': ['1', '16.0235261431676', '0'], 'R_PGI': ['1', '4.86086114649682', '0'], 'R_GLNabc': ['1', '0', '0'], 'R_AKGDH': ['1', '5.06437566148209', '0'], 'R_MDH': ['1', '5.06437566148209', '0'], 'R_EX_fum_e': ['2', '0', '-0.0458323731875524'], 'R_PYK': ['1', '1.75817744410678', '0'], 'M_etoh_c': ['5', '0', '-0.0407398872778244'], 'M_fum_c': ['5', '0', '-0.0483786161424165'], 'M_q8_c': ['5', '0', '0.00254624295486403'], 'M_etoh_e': ['5', '0', '-0.0394667658003924'], 'M_fum_e': ['5', '0', '-0.0458323731875524'], 'R_FRUpts2': ['1', '0', '0'], 'M_nadph_c': ['1', '-8.88178419700125e-16', '0'], 'R_ENO': ['1', '14.7161395687428', '0'], 'R_PIt2r': ['1', '3.21489504768477', '0'], 'R_EX_mal_L_e': ['2', '0', '-0.0458323731875524'], 'R_ACALD': ['1', '-5.84859702315521e-30', '0'], 'M_for_e': ['5', '0', '-0.00763872886459208'], 'M_nad_c': ['5', '0', '0.00763872886459208'], 'M_6pgc_c': ['5', '0', '-0.0916647463751049'], 'R_FORti': ['1', '5.31182206840464e-31', '0'], 'M_co2_c': ['5', '0', '-0'], 'R_PPS': ['2', '0', '-0.00509248590972805'], 'M_co2_e': ['5', '0', '-0'], 'R_EX_succ_e': ['2', '0', '-0.0521979805747125'], 'R_ACONTa': ['1', '6.00724957535033', '0'], 'M_nadh_c': ['1', '5.32907051820075e-15', '0'], 'R_FUM': ['1', '5.06437566148209', '0'], 'R_GND': ['1', '4.95998494457465', '0'], 'R_ACt2r': ['1', '-0', '0'], 'R_PPC': ['1', '2.50430947036873', '0'], 'R_EX_etoh_e': ['2', '0', '-0.0394667658003924'], 'R_AKGt2r': ['1', '-0', '0'], 'R_GLUN': ['2', '0', '-0.00509248590972805']} for key, val in six.iteritems(result): assert val == reference[key]
def __init__(self, problem=None, *args, **kwargs): super(Model, self).__init__(*args, **kwargs) self.configuration = Configuration() if problem is None: self.problem = glp_create_prob() glp_create_index(self.problem) if self.name is not None: glp_set_prob_name(self.problem, str(self.name)) else: try: self.problem = problem glp_create_index(self.problem) except TypeError: raise TypeError("Provided problem is not a valid GLPK model.") row_num = glp_get_num_rows(self.problem) col_num = glp_get_num_cols(self.problem) for i in range(1, col_num + 1): var = Variable( glp_get_col_name(self.problem, i), lb=glp_get_col_lb(self.problem, i), ub=glp_get_col_ub(self.problem, i), problem=self, type=_GLPK_VTYPE_TO_VTYPE[ glp_get_col_kind(self.problem, i)] ) # This avoids adding the variable to the glpk problem super(Model, self)._add_variables([var]) variables = self.variables for j in range(1, row_num + 1): ia = intArray(col_num + 1) da = doubleArray(col_num + 1) nnz = glp_get_mat_row(self.problem, j, ia, da) constraint_variables = [variables[ia[i] - 1] for i in range(1, nnz + 1)] # Since constraint expressions are lazily retrieved from the solver they don't have to be built here # lhs = _unevaluated_Add(*[da[i] * constraint_variables[i - 1] # for i in range(1, nnz + 1)]) lhs = 0 glpk_row_type = glp_get_row_type(self.problem, j) if glpk_row_type == GLP_FX: row_lb = glp_get_row_lb(self.problem, j) row_ub = row_lb elif glpk_row_type == GLP_LO: row_lb = glp_get_row_lb(self.problem, j) row_ub = None elif glpk_row_type == GLP_UP: row_lb = None row_ub = glp_get_row_ub(self.problem, j) elif glpk_row_type == GLP_DB: row_lb = glp_get_row_lb(self.problem, j) row_ub = glp_get_row_ub(self.problem, j) elif glpk_row_type == GLP_FR: row_lb = None row_ub = None else: raise Exception( "Currently, optlang does not support glpk row type %s" % str(glpk_row_type) ) log.exception() if isinstance(lhs, int): lhs = sympy.Integer(lhs) elif isinstance(lhs, float): lhs = sympy.RealNumber(lhs) constraint_id = glp_get_row_name(self.problem, j) for variable in constraint_variables: try: self._variables_to_constraints_mapping[variable.name].add(constraint_id) except KeyError: self._variables_to_constraints_mapping[variable.name] = set([constraint_id]) super(Model, self)._add_constraints( [Constraint(lhs, lb=row_lb, ub=row_ub, name=constraint_id, problem=self, sloppy=True)], sloppy=True ) term_generator = ( (glp_get_obj_coef(self.problem, index), variables[index - 1]) for index in range(1, glp_get_num_cols(problem) + 1) ) self._objective = Objective( _unevaluated_Add( *[_unevaluated_Mul(sympy.RealNumber(term[0]), term[1]) for term in term_generator if term[0] != 0.]), problem=self, direction={GLP_MIN: 'min', GLP_MAX: 'max'}[glp_get_obj_dir(self.problem)]) glp_scale_prob(self.problem, GLP_SF_AUTO)
Example ---------- >>> with TemporaryFilename() as tmp_file_name: >>> with open(tmp_file_name, "w") as tmp_file: >>> tmp_file.write(stuff) >>> with open(tmp_file) as tmp_file: >>> stuff = tmp_file.read() """ def __init__(self, suffix="tmp", content=None): tmp_file = tempfile.NamedTemporaryFile(suffix=suffix, delete=False, mode="w") if content is not None: tmp_file.write(content) self.name = tmp_file.name tmp_file.close() def __enter__(self): return self.name def __exit__(self, type, value, traceback): os.remove(self.name) if __name__ == '__main__': from swiglpk import glp_create_prob, glp_read_lp, glp_get_num_rows problem = glp_create_prob() glp_read_lp(problem, None, "../tests/data/model.lp") print("asdf", glp_get_num_rows(problem)) solution = solve_with_glpsol(problem) print(solution['R_Biomass_Ecoli_core_w_GAM'])
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 _import_problem(self): import swiglpk as glpk if self.verbosity() >= 1: glpk.glp_term_out(glpk.GLP_ON) else: glpk.glp_term_out(glpk.GLP_OFF) # Create a problem instance. p = self.int = glpk.glp_create_prob(); # Set the objective. if self.ext.objective[0] in ("find", "min"): glpk.glp_set_obj_dir(p, glpk.GLP_MIN) elif self.ext.objective[0] is "max": glpk.glp_set_obj_dir(p, glpk.GLP_MAX) else: raise NotImplementedError("Objective '{0}' not supported by GLPK." .format(self.ext.objective[0])) # Set objective function shift if self.ext.objective[1] is not None \ and self.ext.objective[1].constant is not None: if not isinstance(self.ext.objective[1], AffinExp): raise NotImplementedError("Non-linear objective function not " "supported by GLPK.") if self.ext.objective[1].constant.size != (1,1): raise NotImplementedError("Non-scalar objective function not " "supported by GLPK.") glpk.glp_set_obj_coef(p, 0, self.ext.objective[1].constant[0]) # Add variables. # Multideminsional variables are split into multiple scalar variables # represented as matrix columns within GLPK. for varName in self.ext.varNames: var = self.ext.variables[varName] # Add a column for every scalar variable. numCols = var.size[0] * var.size[1] glpk.glp_add_cols(p, numCols) for localIndex, picosIndex \ in enumerate(range(var.startIndex, var.endIndex)): glpkIndex = self._picos2glpk_variable_index(picosIndex) # Assign a name to the scalar variable. scalarName = varName if numCols > 1: x = localIndex // var.size[0] y = localIndex % var.size[0] scalarName += "_{:d}_{:d}".format(x + 1, y + 1) glpk.glp_set_col_name(p, glpkIndex, scalarName) # Assign bounds to the scalar variable. lower, upper = var.bnd.get(localIndex, (None, None)) if lower is not None and upper is not None: if lower == upper: glpk.glp_set_col_bnds( p, glpkIndex, glpk.GLP_FX, lower, upper) else: glpk.glp_set_col_bnds( p, glpkIndex, glpk.GLP_DB, lower, upper) elif lower is not None and upper is None: glpk.glp_set_col_bnds(p, glpkIndex, glpk.GLP_LO, lower, 0) elif lower is None and upper is not None: glpk.glp_set_col_bnds(p, glpkIndex, glpk.GLP_UP, 0, upper) else: glpk.glp_set_col_bnds(p, glpkIndex, glpk.GLP_FR, 0, 0) # Assign a type to the scalar variable. if var.vtype in ("continuous", "symmetric"): glpk.glp_set_col_kind(p, glpkIndex, glpk.GLP_CV) elif var.vtype == "integer": glpk.glp_set_col_kind(p, glpkIndex, glpk.GLP_IV) elif var.vtype == "binary": glpk.glp_set_col_kind(p, glpkIndex, glpk.GLP_BV) else: raise NotImplementedError("Variable type '{0}' not " "supported by GLPK.".format(var.vtype())) # Set objective function coefficient of the scalar variable. if self.ext.objective[1] is not None \ and var in self.ext.objective[1].factors: glpk.glp_set_obj_coef(p, glpkIndex, self.ext.objective[1].factors[var][localIndex]) # Add constraints. # Multideminsional constraints are split into multiple scalar # constraints represented as matrix rows within GLPK. rowOffset = 1 for constraintNum, constraint in enumerate(self.ext.constraints): if not isinstance(constraint, AffineConstraint): raise NotImplementedError( "Non-linear constraints not supported by GLPK.") # Add a row for every scalar constraint. # Internally, GLPK uses an auxiliary variable for every such row, # bounded by the right hand side of the scalar constraint in a # canonical form. numRows = len(constraint) glpk.glp_add_rows(p, numRows) self._debug("Handling PICOS Constraint: " + str(constraint)) # Split multidimensional constraints into multiple scalar ones. for localConIndex, (glpkVarIndices, coefficients, rhs) in \ enumerate(constraint.sparse_Ab_rows( None, indexFunction = lambda picosVar, i: self._picos2glpk_variable_index(picosVar.startIndex + i))): # Determine GLPK's row index of the scalar constraint. glpkConIndex = rowOffset + localConIndex numColumns = len(glpkVarIndices) # Name the auxiliary variable associated with the current row. if constraint.name: name = constraint.name else: name = "rhs_{:d}".format(constraintNum) if numRows > 1: x = localConIndex // constraint.size[0] y = localConIndex % constraint.size[0] name += "_{:d}_{:d}".format(x + 1, y + 1) glpk.glp_set_row_name(p, glpkConIndex, name) # Assign bounds to the auxiliary variable. if constraint.is_equality(): glpk.glp_set_row_bnds(p, glpkConIndex, glpk.GLP_FX, rhs,rhs) elif constraint.is_increasing(): glpk.glp_set_row_bnds(p, glpkConIndex, glpk.GLP_UP, 0, rhs) elif constraint.is_decreasing(): glpk.glp_set_row_bnds(p, glpkConIndex, glpk.GLP_LO, rhs, 0) else: assert False, "Unexpected constraint relation." # Set coefficients for current row. # Note that GLPK requires a glpk.intArray containing column # indices and a glpk.doubleArray of same size containing the # coefficients for the listed column index. The first element # of both arrays (with index 0) is skipped by GLPK. glpkVarIndicesArray = glpk.intArray(numColumns + 1) for i in range(numColumns): glpkVarIndicesArray[i + 1] = glpkVarIndices[i] coefficientsArray = glpk.doubleArray(numColumns + 1) for i in range(numColumns): coefficientsArray[i + 1] = coefficients[i] glpk.glp_set_mat_row(p, glpkConIndex, numColumns, glpkVarIndicesArray, coefficientsArray) rowOffset += numRows