def traj_optim_static(paths, tree): path, envs, modes, mnps = paths guard_index = [0] n = len(modes) v_init = np.zeros((n, 3)) for i in range(1, n): if not np.all(modes[i] == modes[i - 1]): guard_index.append(i) elif len(envs[i]) != 0: if not envs[i][0].is_same(envs[i - 1][0]): guard_index.append(i) elif not (mnps[i][0].is_same(mnps[i - 1][0]) and mnps[i][1].is_same(mnps[i - 1][1])): # manipulator change guard_index.append(i) g_v = np.identity(3) g_v[0:2, 0:2] = config2trans(path[i - 1])[0:2, 0:2] v_init[i - 1] = np.dot(g_v.T, np.array(path[i]) - np.array(path[i - 1])) #guard_index.append(len(modes)-1) guard_index = np.unique(guard_index) Gs = dict() hs = dict() As = dict() bs = dict() for i in range(len(path)): G, h, A, b = contact_mode_constraints(path[i], mnps[i], envs[i], modes[i], tree.world, tree.mnp_mu, tree.env_mu, tree.mnp_fn_max) gid = np.any(G[:, 0:3], axis=1) aid = np.any(A[:, 0:3], axis=1) Gs[i] = G[gid, 0:3] hs[i] = h[gid].flatten() As[i] = A[aid, 0:3] bs[i] = b[aid].flatten() modeconstraints = (Gs, hs, As, bs) q_goal = np.array(tree.x_goal) opt_prob = Optimization('Trajectory Optimization', obj_fun) x_init = np.hstack((np.array(path).flatten(), v_init.flatten())) cs = constraints(x_init, path, Gs, hs, As, bs, guard_index) opt_prob.addVarGroup('x', n * 6, 'c', value=x_init, lower=-10, upper=10) opt_prob.addObj('f') opt_prob.addConGroup('g', len(cs), 'i', lower=0.0, upper=10000.0) print(opt_prob) slsqp = SLSQP() #slsqp.setOption('IPRINT', -1) slsqp(opt_prob, sens_type='FD', goal=q_goal, path=path, modecons=modeconstraints, guard_index=guard_index) print(opt_prob.solution(0)) qs = [opt_prob.solution(0)._variables[i].value for i in range(n * 3)] return qs
def solveOpt(int_domain, J, a, model, u0, sign): ''' INPUT: int_domain = J = a = model = u0 = sign = OUTPUT: opt_prob = ''' def objfun(u, **kwargs): '''objfun defines the objective function and the constraints (equality and inequality) of the optiization problem''' # 1) extract paraeters int_domain = kwargs['int_domain'] J = kwargs['J'] x = kwargs['a'] model = kwargs['model'] sign = kwargs['sign'] # 2) define objective function funz = np.trapz(x=int_domain, y=J * model.pf(int_domain, u, x)) g = [0] * 2 # 3) budget constraint g[0] = u.sum() - 1 # 4) VaR constarint W = model.W sigmaMax = model.VaR / norm.ppf(1 - model.alpha) g[1] = -sigmaMax + np.sqrt(W.dot(u).dot(u)) fail = 0 return sign * funz, g, fail opt_prob = Optimization('ODAA problem', objfun) opt_prob.addObj('funz') opt_prob.addCon('budget const', 'e') opt_prob.addCon('VaR const', 'i') slsqp = SLSQP() # instantiate Optimizer slsqp.setOption('IPRINT', -1) opt_prob.addVarGroup('u', model.M, 'c', lower=np.zeros(model.M), upper=np.ones(model.M), value=u0) #print opt_prob # print optimization problem slsqp(opt_prob, sens_type='FD', int_domain=int_domain, J=J, a=a, model=model, sign=sign) #print opt_prob.solution(0) # print solution return opt_prob
def __init__(self, nlpy_model, **kwargs): """ :parameters: :nlpy_model: nonlinear problem from the NLPModel class or from AmplModel class """ if nlpy_model.__module__ == 'nlpy.model.amplpy': print 'AMPL model' print "AMPL isn't handling complex values" print "Choose 'FD' or 'opt_prob.grad_func' for sensitivity" # Initialize model. Optimization.__init__(self, nlpy_model.name, lambda x: (self.nlpy_model.obj(x), self.nlpy_model.cons(x).tolist(),0), var_set={}, obj_set={}, con_set={}, use_groups=False, **kwargs) self.nlpy_model = nlpy_model self.addObj('f') # Assigning lower and upper bounds on variables for i in range(0,self.nlpy_model.n): if i in self.nlpy_model.lowerB: self.addVar("x"+ "%d"%(i+1), lower=self.nlpy_model.Lvar[i], upper=numpy.inf, value=self.nlpy_model.x0[i]) elif i in self.nlpy_model.upperB: self.addVar("x"+ "%d"%(i+1), lower=-numpy.inf, upper=self.nlpy_model.Uvar[i], value=self.nlpy_model.x0[i]) elif i in self.nlpy_model.rangeB: self.addVar("x"+ "%d"%(i+1), lower=self.nlpy_model.Lvar[i], upper=self.nlpy_model.Uvar[i], value=self.nlpy_model.x0[i]) elif i in self.nlpy_model.freeB: self.addVar("x"+ "%d"%(i+1), value=self.nlpy_model.x0[i], lower=-numpy.inf, upper=numpy.inf) # Assigning lower and upper bounds on constraints for i in range(0,nlpy_model.m): if i in nlpy_model.lowerC: self.addCon("g"+"%d"%(i+1), 'i', lower=nlpy_model.Lcon[i], upper=numpy.inf) elif i in nlpy_model.upperC: self.addCon("g"+"%d"%(i+1), 'i', lower=-numpy.inf, upper=nlpy_model.Ucon[i]) elif i in nlpy_model.rangeC: self.addCon("g"+"%d"%(i+1), 'i', lower=nlpy_model.Lcon[i], upper=nlpy_model.Ucon[i]) elif i in nlpy_model.equalC: self.addCon("g"+"%d"%(i+1), 'e', equal=nlpy_model.Lcon[i])
def translateProgramToPyOpt(dfovecProgram): # Currently only handles inequality def objfunc(x): f = dfovecProgram.objective(x) g = [] if dfovecProgram.hasInequalityConstraints(): g = dfovecProgram.inequalityConstraints(x) fail = 0 return f, g, fail opt_prob = Optimization('Dfovec problem', objfunc) for i in range(len(dfovecProgram.x0)): opt_prob.addVar('x' + str(i), lower=-1000.0, upper=1000.0, value=dfovecProgram.x0[i]) opt_prob.addObj('f') numIneq = dfovecProgram.getNumInequalityConstraints() opt_prob.addConGroup('g', numIneq, type='i', lower=[-10000] * numIneq, upper=[0] * numIneq) print(opt_prob) return opt_prob
def otimiza(obitos, x0): a, b, c, d = x0 partial_func = partial(problema, obitos=obitos) update_wrapper(partial_func, problema) # Instantiate Optimization Problem opt_prob = Optimization('Rosenbrock Unconstraint Problem', partial_func) # opt_prob.addVarGroup('x', 2, 'c', lower=-1e10, upper=0.5, value=-3.0) # opt_prob.addVar('x1', 'c', lower=-10, upper=10, value=-3.0) # opt_prob.addVar('x2', 'c', lower=-10, upper=10, value=-4.0) opt_prob.addVar('A', 'c', lower=0, upper=2000, value=a) opt_prob.addVar('B', 'c', lower=0, upper=2000, value=b) opt_prob.addVar('C', 'c', lower=0, upper=2, value=c) opt_prob.addVar('D', 'c', lower=0, upper=1, value=d) # opt_prob.addCon('C', type='i', lower=0, upper=5, equal=c) # opt_prob.addCon('D', type='i', lower=0, upper=1, equal=d) opt_prob.addObj('f') # print(opt_prob) # from pyOpt.pySLSQP.pySLSQP import SLSQP # sopt = SLSQP() # sopt.setOption('IPRINT', -1) from pyOpt.pySOLVOPT.pySOLVOPT import SOLVOPT sopt = SOLVOPT() sopt.setOption('iprint', -1) [fstr, xstr, inform] = sopt(opt_prob, sens_type='FD') solution = getlastsolution(opt_prob) print(solution) return xstr, solution
def solveOpt(int_domain, J, a, model, u0, sign): ''' INPUT: int_domain = J = a = model = u0 = sign = OUTPUT: opt_prob = ''' def objfun(u, **kwargs): '''objfun defines optimization problem using the pyOpt sintax''' # 1) extract paraeters int_domain = kwargs['int_domain'] J = kwargs['J'] x = kwargs['a'] model = kwargs['model'] sign = kwargs['sign'] # 2) define objective function and constraints funz = np.trapz(x=int_domain, y=J * model.pf(int_domain, u, x)) g = [] fail = 0 return sign * funz, g, fail opt_prob = Optimization('ODAA problem', objfun) opt_prob.addObj('funz') solver = SLSQP() # choose the solver solver.setOption('IPRINT', -1) opt_prob.addVar('u', 'c', lower=-1, upper=1, value=u0) #print opt_prob # print optimization problem solver(opt_prob, int_domain=int_domain, J=J, a=a, model=model, sign=sign) #print opt_prob.solution(0) # print solution return opt_prob
def infill(self, points, method='error'): ## We'll be making non-permanent modifications to self.X and self.y here, so lets make a copy just in case initX = np.copy(self.X) inity = np.copy(self.y) ## This array will hold the new values we add returnValues = np.zeros([points, self.k], dtype=float) for i in range(points): opt_prob1 = Optimization('InFillPSO', self.errorObjective_normalized) for k in range(self.k): opt_prob1.addVar('{0}'.format(k), 'c', lower=0, upper=1, value=.5) pso1 = ALPSO() pso1.setOption('SwarmSize', 100) pso1.setOption('maxOuterIter', 100) pso1.setOption('stopCriteria', 1) pso1(opt_prob1) newpoint = np.zeros(self.k) for j in range(self.k): newpoint[j] = opt_prob1.solution(0)._variables[j].value returnValues[i][:] = self.inversenormX(newpoint) self.addPoint(returnValues[i], self.predict(returnValues[i]), norm=True) self.updateModel() del (opt_prob1) del (pso1) self.X = np.copy(initX) self.y = np.copy(inity) self.n = len(self.X) self.updateModel() return returnValues
def configure(self, building): # Get building and optimization setup properties self.building = deepcopy(building) self.T, self.states, self.actions, self.disturbances, self.controlLim, self.actionLim, self.comfort, self.occ, self.nvars, self.ncons = self.building.getConfiguration( ) # Define Box Constraints (min/max values) for the control parameters boxConstraints = [] for ii in range(self.nvars): boxConstraints.append(self.controlLim) # Link to the python function calculating the cost and the constraints self.opt_prob = Optimization('SLSQP Constrained Problem', self.wrapSimulation) # Setupt Box Constrains in pyOpt for ii in range(self.nvars): self.opt_prob.addVar('x' + str(ii + 1), 'c', lower=boxConstraints[ii][0], upper=boxConstraints[ii][1], value=self.building.policy[0, ii]) # Setupt Cost Function in pyOpt self.opt_prob.addObj('f') # Setupt Inequality Constraints in pyOpt for ii in range(self.ncons): self.opt_prob.addCon('g' + str(ii + 1), 'i') # Print the Optimization setup print("----------------------------------------") print("----------------------------------------") print("SLSQP Optimization setup:") print(self.opt_prob)
def get_pyopt_optimization(f, g_f, con, g_con, x0, T): opt_prob = Optimization('stoc planner', obj_fun(f, con)) opt_prob.addObj('f') opt_prob.addVarGroup('flat_plan', x0.size, type='c', value = x0, lower = 0., upper = 1.0) opt_prob.addConGroup('g', T, 'e') # opt = SLSQP() # opt = pySNOPT.SNOPT() # opt = PSQP() # opt = CONMIN() opt = ALGENCAN() return opt_prob, opt
g_con[1][0] = x[1] * x[2] * x[3] g_con[1][1] = x[0] * x[2] * x[3] g_con[1][2] = x[0] * x[1] * x[3] g_con[1][3] = x[0] * x[1] * x[2] fail = 0 return g_obj, g_con, fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('HS 071', objfunc) opt_prob.addVar('x1', 'c', value=1., lower=1., upper=5.) opt_prob.addVar('x2', 'c', value=5., lower=1., upper=5.) opt_prob.addVar('x3', 'c', value=5., lower=1., upper=5.) opt_prob.addVar('x4', 'c', value=1., lower=1., upper=5.) opt_prob.addObj('f') opt_prob.addCon('g1', 'e', equal=40.) opt_prob.addCon('g2', 'i', lower=25., upper=numpy.inf) print opt_prob # Instantiate Optimizer (IPOPT) ipopt = IPOPT() # Solve Problem with Optimizer Using Finite Differences ipopt.setOption('output_file', 'ipopt.out')
#============================================================================== # Start Matlab engine #============================================================================== eng = matlab.engine.start_matlab() #Go to directory where matlab file is eng.cd('..') eng.cd('SMA_temperature_strain_driven') # ============================================================================= # # ============================================================================= chord = 1. x_hinge = 0.75 safety = 0.005*chord opt_prob = Optimization('Static model optimization',objfunc) #xs_n opt_prob.addVar('x1', 'c', lower = x_hinge/2. , upper = x_hinge - safety, value = 6.817445e-001) #ys_n opt_prob.addVar('x2', 'c', lower = -.9, upper = -0., value = -5.216475e-001) #xs_p opt_prob.addVar('x3', 'c', lower = x_hinge + safety, upper = chord - safety, value = 9.029895e-001) #ys_p opt_prob.addVar('x4', 'c', lower = 0., upper = .9, value = 8.726738e-001) #xl_n opt_prob.addVar('x5', 'c', lower = x_hinge/2., upper = x_hinge - safety, value = 6.958111e-001) #yl_n opt_prob.addVar('x6', 'c', lower = -.9, upper = 0.9, value = -4.593744e-001) #xl_p opt_prob.addVar('x7', 'c', lower = x_hinge + safety, upper = chord - safety, value = 8.187166e-001) #yl_p
def main(): ########################################### # Define some values ########################################### n_blades = 2 n_elements = 10 radius = unit_conversion.in2m(9.6) / 2 root_cutout = 0.1 * radius dy = float(radius - root_cutout) / n_elements dr = float(1) / n_elements y = root_cutout + dy * np.arange(1, n_elements + 1) r = y / radius pitch = 0.0 airfoils = (('SDA1075_494p', 0.0, 1.0), ) allowable_Re = [ 1000000., 500000., 250000., 100000., 90000., 80000., 70000., 60000., 50000., 40000., 30000., 20000., 10000. ] vehicle_weight = 12.455 max_chord = 0.3 max_chord_tip = 5. alt = 0 tip_loss = True mach_corr = False # Forward flight parameters v_inf = 4. # m/s alpha0 = 0.0454 # Starting guess for trimmed alpha in radians n_azi_elements = 5 # Mission times time_in_hover = 300. # Time in seconds time_in_ff = 500. mission_time = [time_in_hover, time_in_ff] Cl_tables = {} Cd_tables = {} Clmax = {} # Get lookup tables if any(airfoil[0] != 'simple' for airfoil in airfoils): for airfoil in airfoils: Cl_table, Cd_table, Clmax = aero_coeffs.create_Cl_Cd_table( airfoil[0]) Cl_tables[airfoil[0]] = Cl_table Cd_tables[airfoil[0]] = Cd_table Clmax[airfoil[0]] = Clmax # Create list of Cl functions. One for each Reynolds number. Cl_tables (and Cd_tables) will be empty for the # 'simple' case, therefore this will be skipped for the simple case. For the full table lookup case this will be # skipped because allowable_Re will be empty. Cl_funs = {} Cd_funs = {} lift_curve_info_dict = {} if Cl_tables and allowable_Re: Cl_funs = dict( zip(allowable_Re, [ aero_coeffs.get_Cl_fun(Re, Cl_tables[airfoils[0][0]], Clmax[airfoils[0][0]][Re]) for Re in allowable_Re ])) Cd_funs = dict( zip(allowable_Re, [ aero_coeffs.get_Cd_fun(Re, Cd_tables[airfoils[0][0]]) for Re in allowable_Re ])) lift_curve_info_dict = aero_coeffs.create_liftCurveInfoDict( allowable_Re, Cl_tables[airfoils[0][0]]) ########################################### # Set design variable bounds ########################################### # Hover opt 500 gen, 1000 pop, 12.455 N weight, 9.6 in prop chord = np.array([ 0.11923604, 0.2168746, 0.31540216, 0.39822882, 0.42919, 0.35039799, 0.3457828, 0.28567224, 0.23418368, 0.13502483 ]) twist = np.array([ 0.45316866, 0.38457724, 0.38225075, 0.34671967, 0.33151445, 0.28719111, 0.25679667, 0.25099005, 0.19400679, 0.10926302 ]) omega = 3811.03596674 * 2 * np.pi / 60 original = (omega, chord, twist) dtwist = np.array( [twist[i + 1] - twist[i] for i in xrange(len(twist) - 1)]) dchord = np.array( [chord[i + 1] - chord[i] for i in xrange(len(chord) - 1)]) twist0 = twist[0] chord0 = chord[0] omega_start = omega dtwist_start = dtwist dchord_start = dchord twist0_start = twist0 chord0_start = chord0 omega_lower = 2000 * 2 * np.pi / 60 omega_upper = 8000.0 * 2 * np.pi / 60 twist0_lower = 0. * 2 * np.pi / 360 twist0_upper = 60. * 2 * np.pi / 360 chord0_upper = 0.1198 chord0_lower = 0.05 dtwist_lower = -10.0 * 2 * np.pi / 360 dtwist_upper = 10.0 * 2 * np.pi / 360 dchord_lower = -0.1 dchord_upper = 0.1 opt_prob = Optimization('Mission Simulator', objfun) opt_prob.addVar('omega_h', 'c', value=omega_start, lower=omega_lower, upper=omega_upper) opt_prob.addVar('twist0', 'c', value=twist0_start, lower=twist0_lower, upper=twist0_upper) opt_prob.addVar('chord0', 'c', value=chord0_start, lower=chord0_lower, upper=chord0_upper) opt_prob.addVarGroup('dtwist', n_elements - 1, 'c', value=dtwist_start, lower=dtwist_lower, upper=dtwist_upper) opt_prob.addVarGroup('dchord', n_elements - 1, 'c', value=dchord_start, lower=dchord_lower, upper=dchord_upper) opt_prob.addObj('f') opt_prob.addCon('thrust', 'i') opt_prob.addCon('c_tip', 'i') opt_prob.addConGroup('c_lower', n_elements, 'i') opt_prob.addConGroup('c_upper', n_elements, 'i') print opt_prob slsqp = SLSQP() slsqp.setOption('IPRINT', 1) slsqp.setOption('MAXIT', 1000) slsqp.setOption('ACC', 1e-8) fstr, xstr, inform = slsqp(opt_prob, sens_type='FD', n_blades=n_blades, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch, airfoils=airfoils, vehicle_weight=vehicle_weight, max_chord=max_chord, tip_loss=tip_loss, mach_corr=mach_corr, Cl_funs=Cl_funs, Cd_funs=Cd_funs, Cl_tables=Cl_tables, Cd_tables=Cd_tables, allowable_Re=allowable_Re, alt=alt, v_inf=v_inf, alpha0=alpha0, mission_time=mission_time, n_azi_elements=n_azi_elements, lift_curve_info_dict=lift_curve_info_dict, max_chord_tip=max_chord_tip) print opt_prob.solution(0) # pop_size = 300 # max_gen = 500 # opt_method = 'nograd' # nsga2 = NSGA2() # nsga2.setOption('PrintOut', 2) # nsga2.setOption('PopSize', pop_size) # nsga2.setOption('maxGen', max_gen) # nsga2.setOption('pCross_real', 0.85) # nsga2.setOption('xinit', 1) # fstr, xstr, inform = nsga2(opt_prob, n_blades=n_blades, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch, # airfoils=airfoils, vehicle_weight=vehicle_weight, max_chord=max_chord, tip_loss=tip_loss, # mach_corr=mach_corr, Cl_funs=Cl_funs, Cd_funs=Cd_funs, Cl_tables=Cl_tables, # Cd_tables=Cd_tables, allowable_Re=allowable_Re, opt_method=opt_method, alt=alt, # v_inf=v_inf, alpha0=alpha0, mission_time=mission_time, n_azi_elements=n_azi_elements, # pop_size=pop_size, max_gen=max_gen, lift_curve_info_dict=lift_curve_info_dict, # max_chord_tip=max_chord_tip) # print opt_prob.solution(0) # opt_method = 'nograd' # xstart_alpso = np.concatenate((np.array([omega_start, twist0_start, chord0_start]), dtwist_start, dchord_start)) # alpso = ALPSO() # alpso.setOption('xinit', 0) # alpso.setOption('SwarmSize', 200) # alpso.setOption('maxOuterIter', 100) # alpso.setOption('stopCriteria', 0) # fstr, xstr, inform = alpso(opt_prob, xstart=xstart_alpso, n_blades=n_blades, n_elements=n_elements, # root_cutout=root_cutout, radius=radius, dy=dy, dr=dr, y=y, r=r, pitch=pitch, # airfoils=airfoils, thrust=thrust, max_chord=max_chord, tip_loss=tip_loss, # mach_corr=mach_corr, Cl_funs=Cl_funs, Cd_funs=Cd_funs, Cl_tables=Cl_tables, # Cd_tables=Cd_tables, allowable_Re=allowable_Re, opt_method=opt_method) # print opt_prob.solution(0) def get_performance(o, c, t): chord_meters = c * radius prop = propeller.Propeller(t, chord_meters, radius, n_blades, r, y, dr, dy, airfoils=airfoils, Cl_tables=Cl_tables, Cd_tables=Cd_tables) quad = quadrotor.Quadrotor(prop, vehicle_weight) ff_kwargs = { 'propeller': prop, 'pitch': pitch, 'n_azi_elements': n_azi_elements, 'allowable_Re': allowable_Re, 'Cl_funs': Cl_funs, 'Cd_funs': Cd_funs, 'tip_loss': tip_loss, 'mach_corr': mach_corr, 'alt': alt, 'lift_curve_info_dict': lift_curve_info_dict } trim0 = np.array([alpha0, o]) alpha_trim, omega_trim, converged = trim.trim(quad, v_inf, trim0, ff_kwargs) T_ff, H_ff, P_ff = bemt.bemt_forward_flight( quad, pitch, omega_trim, alpha_trim, v_inf, n_azi_elements, alt=alt, tip_loss=tip_loss, mach_corr=mach_corr, allowable_Re=allowable_Re, Cl_funs=Cl_funs, Cd_funs=Cd_funs, lift_curve_info_dict=lift_curve_info_dict) dT_h, P_h = bemt.bemt_axial(prop, pitch, o, allowable_Re=allowable_Re, Cl_funs=Cl_funs, Cd_funs=Cd_funs, tip_loss=tip_loss, mach_corr=mach_corr, alt=alt) return sum(dT_h), P_h, T_ff, P_ff, alpha_trim, omega_trim omega = xstr[0] twist0 = xstr[1] chord0 = xstr[2] dtwist = xstr[3:3 + len(r) - 1] dchord = xstr[3 + len(r) - 1:] twist = calc_twist_dist(twist0, dtwist) chord = calc_chord_dist(chord0, dchord) print "chord = " + repr(chord) print "twist = " + repr(twist) # twist_base = calc_twist_dist(twist0_base, dtwist_base) # chord_base = calc_chord_dist(chord0_base, dchord_base) perf_opt = get_performance(omega, chord, twist) perf_orig = get_performance(original[0], original[1], original[2]) print "omega_orig = " + str(original[0]) print "Hover Thrust of original = " + str(perf_orig[0]) print "Hover Power of original = " + str(perf_orig[1]) print "FF Thrust of original = " + str(perf_orig[2]) print "FF Power of original = " + str(perf_orig[3]) print "Trim original (alpha, omega) = (%f, %f)" % (perf_orig[4], perf_orig[5]) print "omega = " + str(omega * 60 / 2 / np.pi) print "Hover Thrust of optimized = " + str(perf_opt[0]) print "Hover Power of optimized = " + str(perf_opt[1]) print "FF Thrust of optimized = " + str(perf_opt[2]) print "FF Power of optimized = " + str(perf_opt[3]) print "Trim optimized (alpha, omega) = (%f, %f)" % (perf_opt[4], perf_opt[5]) # print "Omega base = " + str(omega_start*60/2/np.pi) # print "Thrust of base = " + str(sum(perf_base[0])) # print "Power of base = " + str(sum(perf_base[1])) # plt.figure(1) plt.plot(r, original[1], '-b') plt.plot(r, chord, '-r') plt.xlabel('radial location') plt.ylabel('c/R') plt.legend(['start', 'opt']) plt.figure(2) plt.plot(r, original[2] * 180 / np.pi, '-b') plt.plot(r, twist * 180 / np.pi, '-r') plt.xlabel('radial location') plt.ylabel('twist') plt.legend(['start', 'opt']) plt.show()
f = x0**2 + x1**2 g = [0.0]*2 g[0] = 3 - x0 g[1] = 2 - x1 fail = 0 return f,g,fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('TOY Constrained Problem',objfunc,use_groups=True) opt_prob.addVarGroup('a',2,'c',value=1.0, lower=0.0, upper=10) opt_prob.delVarGroup('a') opt_prob.addVar('x','c',value=1.0, lower=0.0, upper=10) opt_prob.addVarGroup('y',2,'c',value=1.0, lower=0.0, upper=10) opt_prob.delVarGroup('y') opt_prob.addVarGroup('z',1,'c',value=1.0, lower=0.0, upper=10) opt_prob.addVarGroup('b',5,'c',value=3.0, lower=0.0, upper=10) opt_prob.delVarGroup('b') opt_prob.addObj('f') opt_prob.addCon('g1','i') opt_prob.addCon('g2','i') print(opt_prob) # Instantiate Optimizer (SLSQP) & Solve Problem slsqp = SLSQP()
def theor_variogram(experimental_sv, Sb=(0.01,400), Rb=(2,20), Nb=(0,400), ab=(0,2), vb=(0,1000), candidate_sv=None, candidate_sv_tag=None): ''' Fitting of theoretical variogram Parameters ---------- **experimental_sv** -- Experimental semivariogram ''[x,2]'', lag and \ semivariogram \n **Sb** -- Boundaries on Sill of semivariogram ``(min,max)`` \n **Rb** -- Boundaries on Range of semivariogram ``(min,max)`` \n **Nb** -- Boundaries on Nugget of semivariogram ``(min,max)`` \n **ab** -- Boundaries on Power of semivariogram ``(min,max)`` (only \ valid for power semivariogram) \n **vb** -- Boundaries on Shape parameter of semivariogram ``(min,max)``\ (only valid for matérn type) \n Returns ------- **xopt** -- Vector with optimal semivariogram parameters ``[5]`` \n **ModOpt** -- Pointer to optimal vector location \n **candidate_sv** -- Array with pointer to functions in variogram_fit \ module ''' if candidate_sv is None: # Array with functions to be called from the Variograms library candidate_sv = [variogram_fit.exponential_sv, variogram_fit.gaussian_sv] if candidate_sv_tag is None: # Names of functions for display only candidate_sv_tag = ['Exponential','Gaussian'] # Initial seed for variogram fit sr = random.uniform(Sb[0], Sb[1]) rr = random.uniform(Rb[0], Rb[1]) nr = random.uniform(Nb[0], Nb[1]) ar = random.uniform(ab[0], ab[1]) vr = random.uniform(vb[0], vb[1]) Var = [] Res = [] Mdl = [] # Wrapper of minimisation function (RMSE) for semivariogram fitting def _opt_fun(x,*args): F, g, fail = variogram_fit.fit_function(x, experimental_sv, j,candidate_sv) if F == ERROR_CODE: fail = 1 else: Var.append(x) Res.append(F) Mdl.append(j) return F, g, fail # Optimisation starts to minimise differences between experimental and # theoretical semivariograms for j in xrange(0,len(candidate_sv)): VarProb = Optimization('Variogram Fitting: ' + candidate_sv_tag[j], _opt_fun) VarProb.addObj('RMSE') VarProb.addVar('Sill', 'c', lower=Sb[0], upper=Sb[1], value=sr) VarProb.addVar('Range', 'c', lower=Rb[0], upper=Rb[1], value=rr) VarProb.addVar('Nugget', 'c', lower=Nb[0], upper=Nb[1], value=nr) VarProb.addVar('Exponent (a)', 'c', lower=ab[0], upper=ab[1], value=ar) VarProb.addVar('Rank (v)', 'c', lower=vb[0], upper=vb[1], value=vr) args = (experimental_sv, j, candidate_sv, Var, Res, Mdl) optmz = ALHSO() optmz.setOption('fileout',0) optmz(VarProb) # Get pointer to best semivariogram k = np.argmin(Res) xopt = Var[k] ModOpt = Mdl[k] return xopt, ModOpt, candidate_sv
def getlastsolution(prob: Optimization): new_index = prob.firstavailableindex(prob.getSolSet()) return prob.getSol(new_index - 1)
f = -x[0] * x[1] * x[2] g = [0.0] * 2 g[0] = x[0] + 2. * x[1] + 2. * x[2] - 72.0 g[1] = -x[0] - 2. * x[1] - 2. * x[2] time.sleep(0.5) fail = 0 return f, g, fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('TP37 Constrained Problem', objfunc) opt_prob.addVar('x1', 'c', lower=0.0, upper=42.0, value=10.0) opt_prob.addVar('x2', 'c', lower=0.0, upper=42.0, value=10.0) opt_prob.addVar('x3', 'c', lower=0.0, upper=42.0, value=10.0) opt_prob.addObj('f') opt_prob.addCon('g1', 'i') opt_prob.addCon('g2', 'i') # Instantiate Optimizer (SLSQP) slsqp = SLSQP() slsqp.setOption('IPRINT', -1) # Solve Problem (Without Parallel Gradient) slsqp(opt_prob, sens_type='CS') # end
g=[] fail=1 os.chdir('../..') CASECOUNT+=1 return f,g,fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('Minimize Drag',objfunc) opt_prob.addVar('x1','c',lower=1.,upper=25.,value=12.0) opt_prob.addVar('x2','c',lower=1.,upper=30.,value=15.0) opt_prob.addVar('x3','c',lower=1.,upper=30.,value=15.0) opt_prob.addVar('x4','c',lower=1.,upper=25.,value=12.0) opt_prob.addVar('x5','c',lower=1.,upper=25.,value=12.0) opt_prob.addVar('x6','c',lower=1.,upper=13.,value=6.0) opt_prob.addObj('f') #opt_prob.addCon('g','i') print opt_prob mkdirCommand='mkdir -p workDir' subprocess.call(mkdirCommand,shell=True) # Instantiate Optimizer (NSGA2) & Solve Problem nsga2 = NSGA2()
def variogram_fit(SVExp, Sb=(0.01, 400), Rb=(2, 20), Nb=(0, 400), ab=(0, 2), vb=(0, 1000)): # Array with functions to be called from the Variograms library VarFunArr = [ VariogramFit.SVExponential, VariogramFit.SVGaussian, VariogramFit.SVSpherical, VariogramFit.SVCubic, VariogramFit.SVPentaspherical, VariogramFit.SVSinehole, VariogramFit.SVPower, VariogramFit.SVMatern ] # Names of functions for display only optFunNam = [ 'Exponential', 'Gaussian', 'Spherical', 'Cubic', 'Pentaspherical', 'Sinehole', 'Power', 'Matern' ] # Boundaries semivariogram parameters #Sb = (0.01,400) # Limit for the sill #Rb = (2,20) # Limit for the range #Nb = (0,400) # Limit for the Nugget effect #ab = (0,2) # Limit for a in power variogram #vb = (0,1000) # Limit for Matern v parameters # Initial seed for variogram fit sr = random.uniform(Sb[0], Sb[1]) rr = random.uniform(Rb[0], Rb[1]) nr = random.uniform(Nb[0], Nb[1]) ar = random.uniform(ab[0], ab[1]) vr = random.uniform(vb[0], vb[1]) return sr, rr, nr, ar, vr Var = [] Res = [] Mdl = [] # Wrapper of minimisation function (RMSE) for semivariogram fitting def OptFun(x, *args): F, g, fail = VariogramFit.optFunMaster(x, SVExp, j, VarFunArr) if F == 9999: fail = 1 else: Var.append(x) Res.append(F) Mdl.append(j) return F, g, fail print 'Initialising Variogram fit' print '' # Optimisation starts to minimise differences between experimental and # theoretical semivariograms for j in xrange(0, len(VarFunArr)): print 'Variogram Fitting ' + optFunNam[j] print '' VarProb = Optimization('Variogram Fitting: ' + optFunNam[j], OptFun) VarProb.addObj('RMSE') VarProb.addVar('Sill', 'c', lower=Sb[0], upper=Sb[1], value=sr) VarProb.addVar('Range', 'c', lower=Rb[0], upper=Rb[1], value=rr) VarProb.addVar('Nugget', 'c', lower=Nb[0], upper=Nb[1], value=nr) VarProb.addVar('Exponent (a)', 'c', lower=ab[0], upper=ab[1], value=ar) VarProb.addVar('Rank (v)', 'c', lower=vb[0], upper=vb[1], value=vr) args = (SVExp, j, VarFunArr, Var, Res, Mdl) optmz = ALHSO() optmz(VarProb) print VarProb.solution(0) print '' # Get position of best semivariogram k = numpy.argmin(Res) xopt = Var[k] ModOpt = Mdl[k] del Var del Res del Mdl print 'Theoretical variogram fit - Done!' print '' return xopt, ModOpt, VarFunArr
g_con[2][0] = 4.*x[0] + 2 g_con[2][1] = 2.*x[1] - 1 g_con[2][2] = 2.*x[2] g_con[2][3] = -1. fail = 0 return g_obj,g_con,fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('Constrained Rosen-Suzuki',objfunc) opt_prob.addVar('x1','c',value=1.5) opt_prob.addVar('x2','c',value=1.5) opt_prob.addVar('x3','c',value=1.5) opt_prob.addVar('x4','c',value=1.5) opt_prob.addObj('f') opt_prob.addCon('g1','i') opt_prob.addCon('g2','i') opt_prob.addCon('g3','i') print(opt_prob) # Instantiate Optimizer (CONMIN) conmin = CONMIN() # Solve Problem with Optimizer Using Finite Differences conmin(opt_prob,sens_type='FD')
#============================================================================== # Start Matlab engine #============================================================================== eng = matlab.engine.start_matlab() #Go to directory where matlab file is eng.cd('..') eng.cd('SMA_temperature_strain_driven') # ============================================================================= # # ============================================================================= chord = 1. x_hinge = 0.75 safety = 0.005*chord opt_prob = Optimization('Static model optimization', objfunc) #l_s opt_prob.addVar('x1', 'c', lower = 0.1 , upper = 0.6, value = 0.2) #l_l opt_prob.addVar('x2', 'c', lower = 0.1, upper = 0.6, value = 0.2) #R opt_prob.addVar('x5', 'c', lower = 0.001, upper = 0.03, value = 0.02) # #yl_n # opt_prob.addVar('x6', 'c', lower = -.9, upper = 0.9, value = -4.593744e-001) # #xl_p # opt_prob.addVar('x7', 'c', lower = x_hinge + safety, upper = chord - safety, value = 8.187166e-001) # #yl_p # opt_prob.addVar('x8', 'c', lower = -.9, upper = 0., value = -5.719241e-001) opt_prob.addObj('f') #opt_prob.addCon('g', 'i')
def variogram_fit(SVExp, Sb=(0.01,400), Rb=(2,20), Nb=(0,400), ab=(0,2), vb=(0,1000)): # Array with functions to be called from the Variograms library VarFunArr = [VariogramFit.SVExponential, VariogramFit.SVGaussian, VariogramFit.SVSpherical, VariogramFit.SVCubic, VariogramFit.SVPentaspherical, VariogramFit.SVSinehole, VariogramFit.SVPower, VariogramFit.SVMatern] # Names of functions for display only optFunNam = ['Exponential','Gaussian','Spherical','Cubic', 'Pentaspherical','Sinehole','Power','Matern'] # Boundaries semivariogram parameters #Sb = (0.01,400) # Limit for the sill #Rb = (2,20) # Limit for the range #Nb = (0,400) # Limit for the Nugget effect #ab = (0,2) # Limit for a in power variogram #vb = (0,1000) # Limit for Matern v parameters # Initial seed for variogram fit sr = random.uniform(Sb[0],Sb[1]) rr = random.uniform(Rb[0],Rb[1]) nr = random.uniform(Nb[0],Nb[1]) ar = random.uniform(ab[0],ab[1]) vr = random.uniform(vb[0],vb[1]) return sr, rr, nr, ar, vr Var = [] Res = [] Mdl = [] # Wrapper of minimisation function (RMSE) for semivariogram fitting def OptFun(x,*args): F, g, fail = VariogramFit.optFunMaster(x,SVExp,j,VarFunArr) if F == 9999: fail = 1 else: Var.append(x) Res.append(F) Mdl.append(j) return F, g, fail print 'Initialising Variogram fit' print '' # Optimisation starts to minimise differences between experimental and # theoretical semivariograms for j in xrange(0,len(VarFunArr)): print 'Variogram Fitting ' + optFunNam[j] print '' VarProb = Optimization('Variogram Fitting: ' + optFunNam[j], OptFun) VarProb.addObj('RMSE') VarProb.addVar('Sill','c',lower=Sb[0],upper=Sb[1],value=sr) VarProb.addVar('Range','c',lower=Rb[0],upper=Rb[1],value=rr) VarProb.addVar('Nugget','c',lower=Nb[0],upper=Nb[1],value=nr) VarProb.addVar('Exponent (a)','c',lower=ab[0],upper=ab[1],value=ar) VarProb.addVar('Rank (v)','c',lower=vb[0],upper=vb[1],value=vr) args = (SVExp, j, VarFunArr, Var, Res, Mdl) optmz = ALHSO() optmz(VarProb) print VarProb.solution(0) print '' # Get position of best semivariogram k = numpy.argmin(Res) xopt = Var[k] ModOpt = Mdl[k] del Var del Res del Mdl print 'Theoretical variogram fit - Done!' print '' return xopt, ModOpt, VarFunArr
#============================================================================== # Start Matlab engine #============================================================================== eng = matlab.engine.start_matlab() #Go to directory where matlab file is eng.cd('..') eng.cd('SMA_temperature_strain_driven') # ============================================================================= # # ============================================================================= chord = 1. x_hinge = 0.75 safety = 0.05*chord opt_prob = Optimization('Static model optimization',objfunc) #xs_n opt_prob.addVar('x1', 'c', lower = x_hinge/2. , upper = x_hinge - safety, value = 7.407724e-001) #ys_n opt_prob.addVar('x2', 'c', lower = -.9, upper = -0., value = -3.680615e-001) #xs_p opt_prob.addVar('x3', 'c', lower = x_hinge + safety, upper = chord - safety, value = 9.933211e-001) #ys_p opt_prob.addVar('x4', 'c', lower = 0., upper = .9, value = 6.004423e-001) #xl_n opt_prob.addVar('x5', 'c', lower = x_hinge/2., upper = x_hinge - safety, value = 7.290939e-001) #yl_n opt_prob.addVar('x6', 'c', lower = -.9, upper = 0.9, value = -7.584186e-001) #xl_p opt_prob.addVar('x7', 'c', lower = x_hinge + safety, upper = chord - safety, value = 7.550874e-001) #yl_p
def objfunc(xdict): x = xdict['x'] y = xdict['y'] ff = [ (x - 0.0)**2 + (y - 0.0)**2, (x - 1.0)**2 + (y - 1.0)**2, ] gg = [] fail = False return ff, gg, fail # Instantiate Optimization Problem optProb = Optimization('Rosenbrock function', objfunc, use_groups=True) optProb.addVar('x', 'c', value=0, lower=-600, upper=600) optProb.addVar('y', 'c', value=0, lower=-600, upper=600) optProb.addObj('obj1') optProb.addObj('obj2') # 300 generations will find x=(0,0), 200 or less will find x=(1,1) options = { 'maxGen': 200, } opt = NSGA2(options=options) opt.setOption('PrintOut', 0) opt(optProb) print(optProb.getSol(0))
g[0] = x[0] - 1.0 g[1] = 1.333333333 - x[1] g[2] = 2.666666666 - x[2] time.sleep(0.005) fail = 0 return f, g, fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('MIDACO Toy Problem', objfunc) opt_prob.addVar('x1', 'c', lower=1.0, upper=4.0, value=1.0) opt_prob.addVar('x2', 'c', lower=1.0, upper=4.0, value=1.0) opt_prob.addVar('x3', 'c', lower=1.0, upper=4.0, value=1.0) opt_prob.addVar('x4', 'c', lower=1.0, upper=4.0, value=1.0) opt_prob.addObj('f') opt_prob.addCon('g1', 'e') opt_prob.addCon('g2', 'i') opt_prob.addCon('g3', 'i') # Solve Problem (No-Parallelization) midaco_none = MIDACO() midaco_none.setOption('IPRINT', -1) midaco_none.setOption('MAXEVAL', 50000) midaco_none(opt_prob) if myrank == 0:
f = x[0]**2 + x[1]**2 g = [0.0]*2 g[0] = 3 - x[0] g[1] = 2 - x[1] fail = 0 return f,g,fail # ============================================================================= # # ============================================================================= # Instanciate Optimization Problem opt_prob = Optimization('TOY Constraint Problem',objfunc) opt_prob.addVar('x1','c',value=1.0,lower=0.0,upper=10.0) opt_prob.addVar('x2','c',value=1.0,lower=0.0,upper=10.0) opt_prob.addObj('f') opt_prob.addCon('g1','i') opt_prob.addCon('g2','i') print opt_prob # Instanciate Optimizer (ALPSO) & Solve Problem Storing History slsqp = SLSQP() slsqp.setOption('IFILE','slsqp1.out') slsqp(opt_prob,store_hst=True) print opt_prob.solution(0) # Solve Problem Using Stored History (Warm Start) slsqp.setOption('IFILE','slsqp2.out')
# ============================================================================= # # ============================================================================= def objfunc(x): f = 100*(x[1]-x[0]**2)**2+(1-x[0])**2 g = [] fail = 0 return f,g, fail # ============================================================================= # # ============================================================================= opt_prob = Optimization('Rosenbrock Unconstraint Problem',objfunc) opt_prob.addVar('x1','c',lower=-10.0,upper=10.0,value=-3.0) opt_prob.addVar('x2','c',lower=-10.0,upper=10.0,value=-4.0) opt_prob.addObj('f') print opt_prob # Instantiate Optimizer (PSQP) & Solve Problem psqp = PSQP() psqp.setOption('IPRINT',0) psqp(opt_prob,sens_type='FD') print opt_prob.solution(0) # Instantiate Optimizer (SLSQP) & Solve Problem slsqp = SLSQP() slsqp.setOption('IPRINT',-1) slsqp(opt_prob,sens_type='FD')
g = [0.0]*2 g[0] = x[0] + 2.*x[1] + 2.*x[2] - 72.0 g[1] = -x[0] - 2.*x[1] - 2.*x[2] time.sleep(0.5) fail = 0 return f,g, fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('TP37 Constrained Problem',objfunc) opt_prob.addVar('x1','c',lower=0.0,upper=42.0,value=10.0) opt_prob.addVar('x2','c',lower=0.0,upper=42.0,value=10.0) opt_prob.addVar('x3','c',lower=0.0,upper=42.0,value=10.0) opt_prob.addObj('f') opt_prob.addCon('g1','i') opt_prob.addCon('g2','i') # Solve Problem (No-Parallelization) nlpqlp_none = NLPQLP() nlpqlp_none.setOption('IPRINT',0) nlpqlp_none(opt_prob) if myrank == 0: print(opt_prob.solution(0)) #end
def solveOpt(int_domain,J,x,model,u0): def objfun(u,**kwargs): # 1) extract paraeters int_domain = kwargs['int_domain'] J = kwargs['J'] x = kwargs['x'] model = kwargs['model'] # 2) define objective function f = np.trapz(int_domain,J * model.pf(int_domain,u,x)) g = [0]*2 # 3) budget constraint g[1] = u.sum() - 1 # 4) VaR constarint W = model.W sigmaMax = model.VaR / norm.ppf(1-model.alpha) g[0] = -sigmaMax + np.sqrt(W.dot(u).dot(u)) fail = 0 return f,g,fail opt_prob = Optimization('test problem',objfun) opt_prob.addObj('f') opt_prob.addCon('budget const','e') opt_prob.addCon('VaR const','i') opt_prob.addVarGroup('u',model.M,'c',lower=np.zeros(model.M), upper=np.ones(model.M),value=u0) print opt_prob slsqp = SLSQP() slsqp.setOption('IPRINT',-1) slsqp(opt_prob,sens_type='FD',int_domain=int_domain,J=J,x=x,model=model) print opt_prob.solution(0)
# ============================================================================= # # ============================================================================= def objfunc(x): f = 100 * (x[1] - x[0]**2)**2 + (1 - x[0])**2 g = [] fail = 0 return f, g, fail # ============================================================================= # # ============================================================================= opt_prob = Optimization('Rosenbrock Unconstraint Problem', objfunc) opt_prob.addVar('x1', 'c', lower=-10.0, upper=10.0, value=-3.0) opt_prob.addVar('x2', 'c', lower=-10.0, upper=10.0, value=-4.0) opt_prob.addObj('f') print(opt_prob) # Instantiate Optimizer (PSQP) & Solve Problem psqp = PSQP() psqp.setOption('IPRINT', 0) psqp(opt_prob, sens_type='FD') print(opt_prob.solution(0)) # Instantiate Optimizer (SLSQP) & Solve Problem slsqp = SLSQP() slsqp.setOption('IPRINT', -1) slsqp(opt_prob, sens_type='FD')
dareadx = 0.5 * (h1 + h2) * dedx + 0.5 * e * (dh1dx + dh2dx) return dareadx ################################################################################ dp = crm_togw() design_problem = PyOptOptimization(dp.comm, dp.eval_objcon, dp.eval_objcon_grad, number_of_steps=3) opt_prob = Optimization('crm_togw', design_problem.eval_obj_con) opt_prob.addObj('TOGW') opt_prob.addCon('cruise_lift', type='e') opt_prob.addCon('maneuver_lift', type='e') opt_prob.addCon('area', type='e') opt_prob.addCon('ksfailure', type='i') for i in range(187 - 3): opt_prob.addCon('Smoothness %i a' % i, type='i') opt_prob.addCon('Smoothness %i b' % i, type='i') variables = dp.model.get_variables() for i, var in enumerate(variables): print('i', i)
f = x[0]**2 + x[1]**2 g = [0.0]*2 g[0] = 3 - x[0] g[1] = 2 - x[1] fail = 0 return f,g,fail # ============================================================================= # # ============================================================================= # Instanciate Optimization Problem opt_prob = Optimization('TOY Constrained Problem',objfunc) opt_prob.addVar('x1','c',value=1.0,lower=0.0,upper=10.0) opt_prob.addVar('x2','c',value=1.0,lower=0.0,upper=10.0) opt_prob.addObj('f') opt_prob.addCon('g1','i') opt_prob.addCon('g2','i') print(opt_prob) # Instanciate Optimizer (SLSQP) & Solve Problem Storing History slsqp = SLSQP() slsqp.setOption('IFILE','slsqp1.out') slsqp(opt_prob,store_hst=True) print(opt_prob.solution(0)) # Solve Problem Using Stored History (Warm Start) slsqp.setOption('IFILE','slsqp2.out')
from pyOpt import SOLVOPT from pyOpt import KSOPT from pyOpt import NSGA2 from pyOpt import SDPEN def objfun(x): f = 100 * (x[1] - x[0]**2)**2 + (1 - x[0])**2 g = [] fail = 0 return f, g, fail opt_prob = Optimization('Rosenbrock Unconstrained Problem', objfun) opt_prob.addVar('x1', 'c', lower=-10.0, upper=10.0, value=0.0) opt_prob.addVar('x2', 'c', lower=-10.0, upper=10.0, value=0.0) opt_prob.addObj('f') print opt_prob # Instantiate optimizer (PSQP) and solve problem psqp = PSQP() psqp.setOption('IPRINT', 0) psqp(opt_prob, sens_type='FD') print opt_prob.solution(0) # Instantiate optimizer (SLSQP) and solve problem slsqp = SLSQP() slsqp.setOption('IPRINT', -1) slsqp(opt_prob, sens_type='FD')
g_con[2][0] = 4. * x[0] + 2 g_con[2][1] = 2. * x[1] - 1 g_con[2][2] = 2. * x[2] g_con[2][3] = -1. fail = 0 return g_obj, g_con, fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('Constrained Rosen-Suzuki', objfunc) opt_prob.addVar('x1', 'c', value=1.5) opt_prob.addVar('x2', 'c', value=1.5) opt_prob.addVar('x3', 'c', value=1.5) opt_prob.addVar('x4', 'c', value=1.5) opt_prob.addObj('f') opt_prob.addCon('g1', 'i') opt_prob.addCon('g2', 'i') opt_prob.addCon('g3', 'i') print opt_prob # Instantiate Optimizer (CONMIN) conmin = CONMIN() # Solve Problem with Optimizer Using Finite Differences conmin(opt_prob, sens_type='FD')
#print 'i: %d' % i g[g_index] = num - divisor * mult_const * float( CONS_RHS_MAX_2_php[j - 1]) num = 0 for i in range(len(DM_php)): num = num + DM_php[i] * float(NUTRIENTS_php[j - 2][i]) * x[i] g[g_index + 1] = -(num - divisor * mult_const * float(CONS_LHS_MIN_2_php[j - 1])) #time.sleep(0.5) fail = 0 return f, g, fail opt_prob = Optimization('TP37 Constrained Problem', objfunc) for i in range(1, len(CONS_LHS_MIN_1_php) + 1): x_value = 'x' + str(i) opt_prob.addVar(x_value, 'c', lower=float(CONS_LHS_MIN_1_php[i - 1]), upper=float(CONS_RHS_MAX_1_php[i - 1]), value=1) opt_prob.addObj('f') for i in range(1, len(NUTRIENTS_php) * 2 + 1): g_value = 'g' + str(i) opt_prob.addCon(g_value, 'i') # Instantiate Optimizer (SLSQP)
f = -x[0]*x[1]*x[2] g = [0.0]*2 g[0] = x[0] + 2.*x[1] + 2.*x[2] - 72.0 g[1] = -x[0] - 2.*x[1] - 2.*x[2] fail = 0 return f,g, fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('TP37 Constrained Problem',objfunc) opt_prob.addVar('x1','c',lower=0.0,upper=42.0,value=10.0) opt_prob.addVar('x2','c',lower=0.0,upper=42.0,value=10.0) opt_prob.addVar('x3','c',lower=0.0,upper=42.0,value=10.0) opt_prob.addObj('f') opt_prob.addCon('g1','i') opt_prob.addCon('g2','i') print opt_prob # Instantiate Optimizer (PSQP) & Solve Problem psqp = PSQP() psqp.setOption('IPRINT',0) psqp(opt_prob,sens_type='FD') print opt_prob.solution(0) # Instantiate Optimizer (SLSQP) & Solve Problem
for i in xrange(5): f += -(c[i]*exp(-(1/pi)*((x[0]-a[i])**2 + (x[1]-b[i])**2))*cos(pi*((x[0]-a[i])**2 + (x[1]-b[i])**2))) #end g = [0.0]*1 g[0] = 20.04895 - (x[0]+2.0)**2 - (x[1]+1.0)**2 fail = 0 return f,g,fail # ============================================================================= # # ============================================================================= opt_prob = Optimization('Langermann Function 11',objfunc) opt_prob.addVar('x1','c',lower=-2.0,upper=10.0,value=8.0) opt_prob.addVar('x2','c',lower=-2.0,upper=10.0,value=8.0) opt_prob.addObj('f') opt_prob.addCon('g','i') print opt_prob # Global Optimization nsga2 = NSGA2() nsga2(opt_prob) print opt_prob.solution(0) # Local Optimization Refinement slsqp = SLSQP() slsqp(opt_prob.solution(0)) print opt_prob.solution(0).solution(0)
a3 = kwargs['a3'] f = a1*(x[1]-x[0]**2.)**2. + (a2-x[0])**2. g = [0.0]*2 g[0] = x[0]**2. + x[1]**2.0 - a3 fail = 0 return f,g, fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('Rosenbrock Constrained Problem',objfunc) opt_prob.addVar('x1','c',lower=0.0,upper=1.0,value=0.5) opt_prob.addVar('x2','c',lower=0.0,upper=1.0,value=0.5) opt_prob.addObj('f') opt_prob.addCon('g1','i') print(opt_prob) # Arguments to pass into objfunc a1 = 100.0 a2 = 1.0 a3 = 1.0 # Instantiate Optimizer (SLSQP) & Solve Problem slsqp = SLSQP() slsqp.setOption('IPRINT',-1) slsqp(opt_prob,sens_type='FD',a12=[a1,a2],a3=a3)
a3 = kwargs['a3'] f = a1 * (x[1] - x[0]**2.)**2. + (a2 - x[0])**2. g = [0.0] * 2 g[0] = x[0]**2. + x[1]**2.0 - a3 fail = 0 return f, g, fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('Rosenbrock Constrained Problem', objfunc) opt_prob.addVar('x1', 'c', lower=0.0, upper=1.0, value=0.5) opt_prob.addVar('x2', 'c', lower=0.0, upper=1.0, value=0.5) opt_prob.addObj('f') opt_prob.addCon('g1', 'i') print opt_prob # Arguments to pass into objfunc a1 = 100.0 a2 = 1.0 a3 = 1.0 # Instantiate Optimizer (SLSQP) & Solve Problem slsqp = SLSQP() slsqp.setOption('IPRINT', -1) slsqp(opt_prob, sens_type='FD', a12=[a1, a2], a3=a3)
def runoptimizer(): opt_prob = Optimization('TP37 Constrained Problem',objfun) opt_prob.addObj('LL') opt_prob.addVar('x1','c',lower=0.01,upper=10.0,value=1.0) opt_prob.addVar('x2','c',lower=0.01,upper=10.0,value=1.0) opt_prob.addVar('x3','c',lower=0.01,upper=10.0,value=1.0) opt_prob.addVar('x4','c',lower=0.01,upper=10.0,value=1.0) opt_prob.addConGroup('g', 4, 'i') # sanity check print opt_prob print objfun([1.0,1.0,1.0,1.0]) # other optimization methods can be used here - we use sequential least squares programming slsqp = SLSQP() [fstr, xstr, inform] = slsqp(opt_prob) print opt_prob.solution(0) return [v.value for v in opt_prob.solution(0).getVarSet().values()]
maxiter= comm.bcast(maxiter, root=0) zvariable = comm.bcast(zvariable, root=0) mymodelstruct = comm.bcast(mymodelstruct, root=0) isdrude = comm.bcast(isdrude, root=0) n = comm.bcast(n, root=0) myinputdata = comm.bcast(myinputdata, root=0) z = comm.bcast(z, root=0) pathwithoutsample = comm.bcast(pathwithoutsample, root=0) pathwithsample = comm.bcast(pathwithsample, root=0) except: print("No parallelization") ## Optimization dans le cas PyOpt swarm particle ALPSO without parallelization (also works with parallelization) if algo>1: interm2=0 ## Intermediate variable with a function similar to interm opt_prob = Optimization('Dielectric modeling based on TDS pulse fitting',objfunc) if zvariable==1: opt_prob.addVar('thickness','c',lower=lb[0],upper=up[0],value=drudeinput[0]) interm2=interm2+1 if mymodelstruct==2: #in case of TDCMT opt_prob.addVar('w0 tdcmt','c',lower=lb[0+interm2],upper=up[0+interm2],value=drudeinput[0+interm2]) opt_prob.addVar('tau0','c',lower=lb[1+interm2],upper=up[1+interm2],value=drudeinput[1+interm2]) opt_prob.addVar('tau1','c',lower=lb[2+interm2],upper=up[2+interm2],value=drudeinput[2+interm2]) opt_prob.addVar('tau2','c',lower=lb[3+interm2],upper=up[3+interm2],value=drudeinput[3+interm2]) opt_prob.addVar('delta theta','c',lower=lb[4+interm2],upper=up[4+interm2],value=drudeinput[4+interm2]) interm2=interm2+5 opt_prob.addVar('eps inf','c',lower=lb[0+interm2],upper=up[0+interm2],value=drudeinput[0+interm2]) if isdrude==1: opt_prob.addVar('omega p','c',lower=lb[1+interm2],upper=up[1+interm2],value=drudeinput[1+interm2])
def train(self, optimizer='pso'): #Define the optimization problem for training the kriging model opt_prob = Optimization('Surrogate Test', self.fittingObjective) for i in range(self.k): opt_prob.addVar('theta%d' % i, 'c', lower=1e-3, upper=1e2, value=.1) for i in range(self.k): opt_prob.addVar('pl%d' % i, 'c', lower=1.5, upper=2, value=2) opt_prob.addVar('lambda', 'c', lower=1e-5, upper=1, value=1) opt_prob.addObj('f') opt_prob.addCon('g1', 'i') if optimizer == 'pso': optimizer = ALPSO() optimizer.setOption('SwarmSize', 150) optimizer.setOption('maxOuterIter', 150) optimizer.setOption('stopCriteria', 1) optimizer.setOption('filename', '{0}Results.log'.format(self.name)) if optimizer == 'ga': optimizer = NSGA2() optimizer.setOption('PopSize', (4 * 50)) while True: try: self.trainingOptimizer(optimizer, opt_prob) except Exception as e: print e print 'Error traning Model, restarting the optimizer with a larger population' if optimizer == 'ga': optimizer.setOption('SwarmSize', 200) optimizer.setOption('maxOuterIter', 100) optimizer.setOption('stopCriteria', 1) if optimizer == 'ga': optimizer.setOption('PopSize', 400) else: break
g_con[2][0] = 4.0 * x[0] + 2 g_con[2][1] = 2.0 * x[1] - 1 g_con[2][2] = 2.0 * x[2] g_con[2][3] = -1.0 fail = 0 return g_obj, g_con, fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization("Constrained Rosen-Suzuki", objfunc) opt_prob.addVar("x1", "c", value=1.5) opt_prob.addVar("x2", "c", value=1.5) opt_prob.addVar("x3", "c", value=1.5) opt_prob.addVar("x4", "c", value=1.5) opt_prob.addObj("f") opt_prob.addCon("g1", "i") opt_prob.addCon("g2", "i") opt_prob.addCon("g3", "i") print opt_prob # Instantiate Optimizer (CONMIN) conmin = CONMIN() # Solve Problem with Optimizer Using Finite Differences conmin(opt_prob, sens_type="FD")
def execute(self): """pyOpt execution. Note that pyOpt controls the execution, and the individual optimizers control the iteration.""" self.pyOpt_solution = None opt_prob = Optimization(self.title, self.objfunc, var_set={}, obj_set={}, con_set={}) # Add all parameters self.param_type = {} for name, param in self.get_parameters().iteritems(): # We need to identify Enums, Lists, Dicts metadata = param.get_metadata()[0][1] values = param.evaluate() # Assuming uniform enumerated, discrete, or continuous for now. val = values[0] choices = [] if "values" in metadata and isinstance(metadata["values"], (list, tuple, array, set)): vartype = "d" choices = metadata["values"] elif isinstance(val, bool): vartype = "d" choices = [True, False] elif isinstance(val, (int, int32, int64)): vartype = "i" elif isinstance(val, (float, float32, float64)): vartype = "c" else: msg = "Only continuous, discrete, or enumerated variables" " are supported. %s is %s." % ( name, type(val), ) self.raise_exception(msg, ValueError) self.param_type[name] = vartype names = param.names lower_bounds = param.get_low() upper_bounds = param.get_high() for i in range(param.size): opt_prob.addVar( names[i], vartype, lower=lower_bounds[i], upper=upper_bounds[i], value=values[i], choices=choices ) # Add all objectives for name in self.get_objectives(): opt_prob.addObj(name) # Add all equality constraints for name in self.get_eq_constraints(): opt_prob.addCon(name, type="e") # Add all inequality constraints for name in self.get_ineq_constraints(): opt_prob.addCon(name, type="i") # Instantiate the requested optimizer optimizer = self.optimizer try: exec ("from pyOpt import %s" % optimizer) except ImportError: msg = "Optimizer %s is not available in this installation." % optimizer self.raise_exception(msg, ImportError) optname = vars()[optimizer] opt = optname() # Set optimization options for option, value in self.options.iteritems(): opt.setOption(option, value) # Execute the optimization problem if self.pyopt_diff: # Use pyOpt's internal finite difference opt(opt_prob, sens_type="FD") else: # Use OpenMDAO's differentiator for the gradient opt(opt_prob, sens_type=self.gradfunc) # Print results if self.print_results: print opt_prob.solution(0) # Pull optimal parameters back into framework and re-run, so that # framework is left in the right final state dvals = [] for i in range(0, len(opt_prob.solution(0)._variables)): dvals.append(opt_prob.solution(0)._variables[i].value) # Integer parameters come back as floats, so we need to round them # and turn them into python integers before setting. if "i" in self.param_type.values(): for j, param in enumerate(self.get_parameters().keys()): if self.param_type[param] == "i": dvals[j] = int(round(dvals[j])) self.set_parameters(dvals) self.run_iteration() self.record_case() # Save the most recent solution. self.pyOpt_solution = opt_prob.solution(0)
(x[1] - b[i])**2)) * cos(pi * ((x[0] - a[i])**2 + (x[1] - b[i])**2))) #end g = [0.0] * 1 g[0] = 20.04895 - (x[0] + 2.0)**2 - (x[1] + 1.0)**2 fail = 0 return f, g, fail # ============================================================================= # # ============================================================================= opt_prob = Optimization('Langermann Function 11', objfunc) opt_prob.addVar('x1', 'c', lower=-2.0, upper=10.0, value=8.0) opt_prob.addVar('x2', 'c', lower=-2.0, upper=10.0, value=8.0) opt_prob.addObj('f') opt_prob.addCon('g', 'i') print(opt_prob) # Global Optimization nsga2 = NSGA2() nsga2(opt_prob) print(opt_prob.solution(0)) # Local Optimization Refinement slsqp = SLSQP() slsqp(opt_prob.solution(0)) print(opt_prob.solution(0).solution(0))
f = x0**2 + x1**2 g = [0.0] * 2 g[0] = 3 - x0 g[1] = 2 - x1 fail = 0 return f, g, fail # ============================================================================= # # ============================================================================= # Instantiate Optimization Problem opt_prob = Optimization('TOY Constraint Problem', objfunc, use_groups=True) opt_prob.addVarGroup('a', 2, 'c', value=1.0, lower=0.0, upper=10) opt_prob.delVarGroup('a') opt_prob.addVar('x', 'c', value=1.0, lower=0.0, upper=10) opt_prob.addVarGroup('y', 2, 'c', value=1.0, lower=0.0, upper=10) opt_prob.delVarGroup('y') opt_prob.addVarGroup('z', 1, 'c', value=1.0, lower=0.0, upper=10) opt_prob.addVarGroup('b', 5, 'c', value=3.0, lower=0.0, upper=10) opt_prob.delVarGroup('b') opt_prob.addObj('f') opt_prob.addCon('g1', 'i') opt_prob.addCon('g2', 'i') print opt_prob # Instantiate Optimizer (PSQP) & Solve Problem slsqp = SLSQP()
y2 = coordinates[:][1] f = 0 for i in range(N-1): f += abs(y1[i]*100 - y2[i]*100)**2 g = [] fail = 0 return f, g, fail # ============================================================================= # # ============================================================================= opt_prob = Optimization('CST Parameterization', objfunc) opt_prob.addVar('x1','c', lower=-2.0,upper=2.0, value=-1.0) opt_prob.addVar('x2','c', lower=-2.0,upper=2.0, value=-1.0) opt_prob.addVar('x3','c', lower=-2.0,upper=2.0, value=-1.0) opt_prob.addVar('x4','c', lower=-2.0,upper=2.0, value=-1.0) opt_prob.addVar('x5','c', lower=-2.0, upper=2.0, value=1.0) opt_prob.addVar('x6','c', lower=-2.0, upper=2.0, value=1.0) opt_prob.addVar('x7','c', lower=-2.0, upper=2.0, value=1.0) opt_prob.addVar('x8','c', lower=-2.0, upper=2.0, value=1.0) opt_prob.addObj('f') print opt_prob # Instantiate Optimizer (SLSQP) & Solve Problem slsqp = SLSQP() slsqp.setOption('IPRINT',-1) slsqp(opt_prob, sens_type='FD')
# ============================================================================= # # ============================================================================= def objfunc(x): f = 100 * (x[1] - x[0]**2)**2 + (1 - x[0])**2 g = [] fail = 0 return f, g, fail # ============================================================================= # # ============================================================================= opt_prob = Optimization('Rosenbrock Unconstraint Problem', objfunc) #List of references in the queue with their priority order opt_prob.addVar('x1', 'c', lower=-10.0, upper=10.0, value=-3.0) #The Processing capacity opt_prob.addVar('x2', 'c', lower=-10.0, upper=10.0, value=-4.0) opt_prob.addObj('f') #Constraints #Each queue has a given processing capacity expressed in number of workers #Each worker can process 35 number of references per week #Notice period to change the number of workers for one step is 8 weeks #Max total capacity increase is 20% of nominal capacity #Fast-lange capacity is fixed equal to 10% planned capacity #Cost to be added in objective function for revising one date # is equal to 1000 * number of overdue days print opt_prob
def objfunc_3(x): f1 = x[0] - x[1] f2 = x[0] + x[1] f = (f1, f2) fail = 0 g = [] return f,(g, g), (fail, fail) # ============================================================================= # # ============================================================================= chord = 1. x_hinge = 0.75 safety = 0.005*chord opt_prob = Optimization('main', (objfunc_1, objfunc_2)) opt_prob.addObj("f1") opt_prob.addObj("f2") #xs_n opt_prob.addVar('x1', 'c', lower = -1 , upper = 1, value = 6.817445e-001) #ys_n opt_prob.addVar('x2', 'c', lower = -1, upper = 1, value = -5.216475e-001) #opt_prob.addObj('2', objfunc_2) print opt_prob # Global Optimization nsga2 = NSGA2() nsga2.setOption('PopSize', 10) nsga2.setOption('maxGen', 10) nsga2(opt_prob)
def execute(self): """pyOpt execution. Note that pyOpt controls the execution, and the individual optimizers control the iteration.""" self.pyOpt_solution = None opt_prob = Optimization(self.title, self.objfunc, var_set={}, obj_set={}, con_set={}) # Add all parameters for name, param in self.get_parameters().iteritems(): val = param.evaluate() # We need to identify Enums, Lists, Dicts metadata = param.get_metadata()[0][1] # enumerated, discrete or continuous choices = [] if ('values' in metadata and \ isinstance(metadata['values'],(list, tuple, array, set))): vartype = 'd' choices = metadata['values'] elif isinstance(val, bool): vartype = 'd' choices = [True, False] elif isinstance(val, (int, int32, int64)): vartype = 'i' elif isinstance(val, (float, float32, float64)): vartype = 'c' else: msg = 'Only continuous, descrete, or enumerated variables ' + \ 'are supported. %s is %s.' % (name, type(val)) self.raise_exception(msg, ValueError) opt_prob.addVar(name, vartype, lower=param.low, upper=param.high, value=val, choices=choices) # Add all objectives for name in self.get_objectives().keys(): opt_prob.addObj(name) # Add all equality constraints for name in self.get_eq_constraints().keys(): opt_prob.addCon(name, type='e') # Add all inequality constraints for name in self.get_ineq_constraints().keys(): opt_prob.addCon(name, type='i') # Instantiate the requested optimizer optimizer = self.optimizer try: exec('from pyOpt import %s' % optimizer) except ImportError: msg = "Optimizer %s is not avialable in this installation." % \ optimizer self.raise_exception(msg, ImportError) optname = vars()[optimizer] opt = optname() # Set optimization options for option, value in self.options.iteritems(): opt.setOption(option, value) # Execute the optimization problem if self.differentiator: # Use OpenMDAO's differentiator for the gradient opt(opt_prob, sens_type=self.gradfunc) else: # Use pyOpt's internal finite difference opt(opt_prob, sens_type='FD') # Print results if self.print_results: print opt_prob.solution(0) # Pull optimal parameters back into framework and re-run, so that # framework is left in the right final state dvals = [] for i in range(0, len(opt_prob.solution(0)._variables)): dvals.append(opt_prob.solution(0)._variables[i].value) self.set_parameters(dvals) self.run_iteration() # Save the most recent solution. self.pyOpt_solution = opt_prob.solution(0)
if __name__ == "__main__": print "running deterministic optimization " # Physical problem rho = 0.2836 # lb/in^3 L = 5.0 # in P = 25000.0 # lb E = 30.0e6 # psi ys = 36260.0 # psi fs = 1.5 dtruss = TwoBarTruss(rho, L, P, E, ys, fs) # Optimization Problem optproblem = TwoBarTrussOpt(MPI.COMM_WORLD, dtruss) opt_prob = Optimization(args.logfile, optproblem.evalObjCon) # Add functions opt_prob.addObj('weight') opt_prob.addCon('buckling-bar1', type='i') opt_prob.addCon('failure-bar1', type='i') opt_prob.addCon('failure-bar2', type='i') # Add variables opt_prob.addVar('area-1', type='c', value=1.0, lower=1.0e-3, upper=2.0) opt_prob.addVar('area-2', type='c', value=1.0, lower=1.0e-3, upper=2.0) opt_prob.addVar('height', type='c', value=4.0, lower=4.0, upper=10.0) # Optimization algorithm if args.algorithm == 'ALGENCAN': opt = ALGENCAN()
def optimizeTrajectory(self, plot_func=None): # use non-linear optimization to find parameters for minimal # condition number trajectory self.plot_func = plot_func if self.config['showOptimizationGraph']: self.initGraph() ## describe optimization problem with pyOpt classes from pyOpt import Optimization from pyOpt import ALPSO, SLSQP # Instanciate Optimization Problem opt_prob = Optimization('Trajectory optimization', self.objective_func) opt_prob.addObj('f') # add variables, define bounds # w_f - pulsation opt_prob.addVar('wf', 'c', value=self.wf_init, lower=self.wf_min, upper=self.wf_max) # q - offsets for i in range(self.dofs): opt_prob.addVar('q_%d'%i,'c', value=self.qinit[i], lower=self.qmin[i], upper=self.qmax[i]) # a, b - sin/cos params for i in range(self.dofs): for j in range(self.nf[0]): opt_prob.addVar('a{}_{}'.format(i,j), 'c', value=self.ainit[i][j], lower=self.amin, upper=self.amax) for i in range(self.dofs): for j in range(self.nf[0]): opt_prob.addVar('b{}_{}'.format(i,j), 'c', value=self.binit[i][j], lower=self.bmin, upper=self.bmax) # add constraint vars (constraint functions are in obfunc) if self.config['minVelocityConstraint']: opt_prob.addConGroup('g', self.dofs*5, 'i') else: opt_prob.addConGroup('g', self.dofs*4, 'i') #print opt_prob initial = [v.value for v in list(opt_prob._variables.values())] if self.config['useGlobalOptimization']: ### optimize using pyOpt (global) opt = ALPSO() #augmented lagrange particle swarm optimization opt.setOption('stopCriteria', 0) opt.setOption('maxInnerIter', 3) opt.setOption('maxOuterIter', self.config['globalOptIterations']) opt.setOption('printInnerIters', 1) opt.setOption('printOuterIters', 1) opt.setOption('SwarmSize', 30) opt.setOption('xinit', 1) #TODO: how to properly limit max number of function calls? # no. func calls = (SwarmSize * inner) * outer + SwarmSize self.iter_max = opt.getOption('SwarmSize') * opt.getOption('maxInnerIter') * opt.getOption('maxOuterIter') + opt.getOption('SwarmSize') # run fist (global) optimization try: #reuse history opt(opt_prob, store_hst=False, hot_start=True, xstart=initial) except NameError: opt(opt_prob, store_hst=False, xstart=initial) print(opt_prob.solution(0)) ### pyOpt local # after using global optimization, get more exact solution with # gradient based method init optimizer (only local) opt2 = SLSQP() #sequential least squares opt2.setOption('MAXIT', self.config['localOptIterations']) if self.config['verbose']: opt2.setOption('IPRINT', 0) # TODO: amount of function calls depends on amount of variables and iterations to approximate gradient # iterations are probably steps along the gradient. How to get proper no. of func calls? self.iter_max = "(unknown)" if self.config['useGlobalOptimization']: if self.last_best_sol is not None: #use best constrained solution for i in range(len(opt_prob._variables)): opt_prob._variables[i].value = self.last_best_sol[i] else: #reuse previous solution for i in range(len(opt_prob._variables)): opt_prob._variables[i].value = opt_prob.solution(0).getVar(i).value opt2(opt_prob, store_hst=False, sens_step=0.1) else: try: #reuse history opt2(opt_prob, store_hst=True, hot_start=True, sens_step=0.1) except NameError: opt2(opt_prob, store_hst=True, sens_step=0.1) local_sol = opt_prob.solution(0) if not self.config['useGlobalOptimization']: print(local_sol) local_sol_vec = np.array([local_sol.getVar(x).value for x in range(0,len(local_sol._variables))]) if self.last_best_sol is not None: local_sol_vec = self.last_best_sol print("using last best constrained solution instead of given solver solution.") sol_wf, sol_q, sol_a, sol_b = self.vecToParams(local_sol_vec) print("testing final solution") self.iter_cnt = 0 self.objective_func(local_sol_vec) print("\n") self.trajectory.initWithParams(sol_a, sol_b, sol_q, self.nf, sol_wf) if self.config['showOptimizationGraph']: plt.ioff() return self.trajectory