#!/usr/local/bin/python ''' pyPSQP - A Python pyOpt interface to PSQP. Copyright (c) 2008-2011 by pyOpt Developers All rights reserved. Revision: 1.0 $Date: 30/12/2010 21:00$ Tested on: --------- Win32 with g77 Linux with pathf95 Linux with gfortran Linux with g77 Developers: ----------- - Dr. Ruben E. Perez (RP) History ------- v. 1.0 - Initial Class Creation (RP, 2010) ''' __version__ = '$Revision: $' ''' To Do: - ''' # ============================================================================= # PSQP Library # ============================================================================= try: import psqp except: raise ImportError('PSQP shared library failed to import') #end # ============================================================================= # Standard Python modules # ============================================================================= import os, sys import copy, time # ============================================================================= # External Python modules # ============================================================================= import numpy # ============================================================================= # Extension modules # ============================================================================= from pyOpt import Optimizer from pyOpt import History from pyOpt import Gradient # ============================================================================= # Misc Definitions # ============================================================================= inf = 10.E+20 # define a value for infinity # ============================================================================= eps = 1.0 # define a value for machine precision while ((eps/2.0 + 1.0) > 1.0): eps = eps/2.0 #end eps = 2.0*eps #eps = math.ldexp(1,-52) # ============================================================================= # PSQP Optimizer Class # ============================================================================= class PSQP(Optimizer): ''' PSQP Optimizer Class - Inherited from Optimizer Abstract Class ''' def __init__(self, pll_type=None, *args, **kwargs): ''' PSQP Optimizer Class Initialization **Keyword arguments:** - pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None Documentation last updated: Feb. 16, 2010 - Peter W. Jansen ''' # if (pll_type == None): self.poa = False elif (pll_type.upper() == 'POA'): self.poa = True else: raise ValueError("pll_type must be either None or 'POA'") #end # name = 'PSQP' category = 'Local Optimizer' def_opts = { 'XMAX':[float,1e16], # Maximum Stepsize 'TOLX':[float,1e-16], # Variable Change Tolerance 'TOLC':[float,1e-6], # Constraint Violation Tolerance 'TOLG':[float,1e-6], # Lagrangian Gradient Tolerance 'RPF':[float,1e-4], # Penalty Coefficient 'MIT':[int,1000], # Maximum Number of Iterations 'MFV':[int,2000], # Maximum Number of Function Evaluations 'MET':[int,2], # Variable Metric Update (1 - BFGS, 2 - Hoshino) 'MEC':[int,2], # Negative Curvature Correction (1 - None, 2 - Powell's Correction) 'IPRINT':[int,2], # Output Level (0 - None, 1 - Final, 2 - Iter) 'IOUT':[int,6], # Output Unit Number 'IFILE':[str,'PSQP.out'], # Output File Name } informs = { 1 : 'Change in design variable was less than or equal to tolerance', 2 : 'Change in objective function was less than or equal to tolerance', 3 : 'Objective function less than or equal to tolerance', 4 : 'Maximum constraint value is less than or equal to tolerance', 11 : 'Maximum number of iterations exceeded', 12 : 'Maximum number of function evaluations exceeded', 13 : 'Maximum number of gradient evaluations exceeded', -6 : 'Termination criterion not satisfied, but obtained point is acceptable', #<0 : 'Method failed', } Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs) def __solve__(self, opt_problem={}, sens_type='FD', store_sol=True, store_hst=False, hot_start=False, disp_opts=False, sens_mode='', sens_step={}, *args, **kwargs): ''' Run Optimizer (Optimize Routine) **Keyword arguments:** - opt_problem -> INST: Optimization instance - sens_type -> STR/FUNC: Gradient type, *Default* = 'FD' - store_sol -> BOOL: Store solution in Optimization class flag, *Default* = True - disp_opts -> BOOL: Flag to display options in solution text, *Default* = False - store_hst -> BOOL/STR: Flag/filename to store optimization history, *Default* = False - hot_start -> BOOL/STR: Flag/filename to read optimization history, *Default* = False - sens_mode -> STR: Flag for parallel gradient calculation, *Default* = '' - sens_step -> FLOAT: Sensitivity setp size, *Default* = {} [corresponds to 1e-6 (FD), 1e-20(CS)] Additional arguments and keyword arguments are passed to the objective function call. Documentation last updated: February. 2, 2011 - Ruben E. Perez ''' # if ((self.poa) and (sens_mode.lower() == 'pgc')): raise NotImplementedError("pyPSQP - Current implementation only allows single level parallelization, either 'POA' or 'pgc'") #end if self.poa or (sens_mode.lower() == 'pgc'): try: import mpi4py from mpi4py import MPI except ImportError: print 'pyPSQP: Parallel objective Function Analysis requires mpi4py' #end comm = MPI.COMM_WORLD nproc = comm.Get_size() if (mpi4py.__version__[0] == '0'): Bcast = comm.Bcast elif (mpi4py.__version__[0] == '1'): Bcast = comm.bcast #end self.pll = True self.myrank = comm.Get_rank() else: self.pll = False self.myrank = 0 #end myrank = self.myrank # tmp_file = False def_fname = self.options['IFILE'][1].split('.')[0] if isinstance(store_hst,str): if isinstance(hot_start,str): if (myrank == 0): if (store_hst == hot_start): hos_file = History(hot_start, 'r', self) log_file = History(store_hst+'_tmp', 'w', self, opt_problem.name) tmp_file = True else: hos_file = History(hot_start, 'r', self) log_file = History(store_hst, 'w', self, opt_problem.name) #end #end self.sto_hst = True self.h_start = True elif hot_start: if (myrank == 0): hos_file = History(store_hst, 'r', self) log_file = History(store_hst+'_tmp', 'w', self, opt_problem.name) tmp_file = True #end self.sto_hst = True self.h_start = True else: if (myrank == 0): log_file = History(store_hst, 'w', self, opt_problem.name) #end self.sto_hst = True self.h_start = False #end elif store_hst: if isinstance(hot_start,str): if (hot_start == def_fname): if (myrank == 0): hos_file = History(hot_start, 'r', self) log_file = History(def_fname+'_tmp', 'w', self, opt_problem.name) tmp_file = True #end else: if (myrank == 0): hos_file = History(hot_start, 'r', self) log_file = History(def_fname, 'w', self, opt_problem.name) #end #end self.sto_hst = True self.h_start = True elif hot_start: if (myrank == 0): hos_file = History(def_fname, 'r', self) log_file = History(def_fname+'_tmp', 'w', self, opt_problem.name) tmp_file = True #end self.sto_hst = True self.h_start = True else: if (myrank == 0): log_file = History(def_fname, 'w', self, opt_problem.name) #end self.sto_hst = True self.h_start = False #end else: self.sto_hst = False self.h_start = False #end # gradient = Gradient(opt_problem, sens_type, sens_mode, sens_step, *args, **kwargs) #====================================================================== # PSQP - Objective/Constraint Values Storage #====================================================================== def eval(x): # Variables Groups Handling if opt_problem.use_groups: xg = {} for group in group_ids.keys(): if (group_ids[group][1]-group_ids[group][0] == 1): xg[group] = x[group_ids[group][0]] else: xg[group] = x[group_ids[group][0]:group_ids[group][1]] #end #end xn = xg else: xn = x #end # Flush Output Files self.flushFiles() # Evaluate User Function (Real Valued) fail = 0 if (myrank == 0): if self.h_start: [vals,hist_end] = hos_file.read(ident=['obj', 'con', 'fail']) if hist_end: self.h_start = False hos_file.close() else: [f,g,fail] = [vals['obj'][0][0],vals['con'][0],int(vals['fail'][0][0])] #end #end #end if self.pll: self.h_start = Bcast(self.h_start,root=0) #end if self.h_start and self.pll: [f,g,fail] = Bcast([f,g,fail],root=0) elif not self.h_start: [f,g,fail] = opt_problem.obj_fun(xn, *args, **kwargs) #end # Store History if (myrank == 0): if self.sto_hst: log_file.write(x,'x') log_file.write(f,'obj') log_file.write(g,'con') log_file.write(fail,'fail') #end #end # Objective Assigment if isinstance(f,float): f = [f] #end for i in xrange(len(opt_problem._objectives.keys())): if isinstance(f[i],complex): ff[i] = f[i].astype(float) else: ff[i] = f[i] #end #end # Constraints Assigment i = 0 for j in xrange(len(opt_problem._constraints.keys())): if isinstance(g[j],complex): gg[i] = g[j].astype(float) else: gg[i] = g[j] #end i += 1 #end # Gradients if self.h_start: if (myrank == 0): [vals,hist_end] = hos_file.read(ident=['grad_obj','grad_con']) if hist_end: self.h_start = False hos_file.close() else: dff = vals['grad_obj'][0].reshape((len(opt_problem._objectives.keys()),len(opt_problem._variables.keys()))) dgg = vals['grad_con'][0].reshape((len(opt_problem._constraints.keys()),len(opt_problem._variables.keys()))) #end #end if self.pll: self.h_start = Bcast(self.h_start,root=0) #end if self.h_start and self.pll: [dff,dgg] = Bcast([dff,dgg],root=0) #end #end if not self.h_start: # dff,dgg = gradient.getGrad(x, group_ids, f, g, *args, **kwargs) #end # Store History if self.sto_hst and (myrank == 0): log_file.write(dff,'grad_obj') log_file.write(dgg,'grad_con') #end # Store self.stored_data['x'] = copy.copy(x) self.stored_data['f'] = copy.copy(ff) self.stored_data['g'] = copy.copy(gg) self.stored_data['df'] = copy.copy(dff) self.stored_data['dg'] = copy.copy(dgg) return #====================================================================== # PSQP - Objective Values Function #====================================================================== def pobj(n,x,f): if ((self.stored_data['x'] != x).any()): eval(x) #end ff = self.stored_data['f'] return ff[0] #====================================================================== # PSQP - Constraint Values Function #====================================================================== def pcon(n,k,x,g): if ((self.stored_data['x'] != x).any()): eval(x) #end gg = self.stored_data['g'] return gg[k-1] #====================================================================== # PSQP - Objective Gradients Function #====================================================================== def pdobj(n,x,df): if ((self.stored_data['x'] != x).any()): eval(x) #end df = self.stored_data['df'] return df[0] #====================================================================== # PSQP - Constraint Gradients Function #====================================================================== def pdcon(n,k,x,dg): if ((self.stored_data['x'] != x).any()): eval(x) #end dg = self.stored_data['dg'] return dg[k-1] # Variables Handling nvar = len(opt_problem._variables.keys()) xl = [] xu = [] xi = [] xx = [] for key in opt_problem._variables.keys(): xl.append(opt_problem._variables[key].lower) xu.append(opt_problem._variables[key].upper) xi.append(3) xx.append(opt_problem._variables[key].value) #end xl = numpy.array(xl) xu = numpy.array(xu) xi = numpy.array(xi) xx = numpy.array(xx) # Variables Groups Handling group_ids = {} if opt_problem.use_groups: k = 0 for key in opt_problem._vargroups.keys(): group_len = len(opt_problem._vargroups[key]['ids']) group_ids[opt_problem._vargroups[key]['name']] = [k,k+group_len] k += group_len #end #end # Constraints Handling ncon = len(opt_problem._constraints.keys()) gl = numpy.zeros([ncon], numpy.float) gu = numpy.zeros([ncon], numpy.float) gi = [] gg = [] if ncon > 0: for key in opt_problem._constraints.keys(): if opt_problem._constraints[key].type == 'e': gi.append(5) elif opt_problem._constraints[key].type == 'i': gi.append(2) #end gg.append(opt_problem._constraints[key].value) #end gg.append(0.0) #end gi = numpy.array(gi) gg = numpy.array(gg) # Objective Handling objfunc = opt_problem.obj_fun nobj = len(opt_problem._objectives.keys()) ff = [] for key in opt_problem._objectives.keys(): ff.append(opt_problem._objectives[key].value) #end ff = numpy.array(ff) # Setup argument list values nf = numpy.array([nvar], numpy.int) nc = numpy.array([ncon], numpy.int) cz = numpy.zeros([nf], numpy.float) xmax = numpy.array([self.options['XMAX'][1]], numpy.float) tolx = numpy.array([self.options['TOLX'][1]], numpy.float) tolc = numpy.array([self.options['TOLC'][1]], numpy.float) tolg = numpy.array([self.options['TOLG'][1]], numpy.float) rpf = numpy.array([self.options['RPF'][1]], numpy.float) cmax = numpy.array([0], numpy.float) gmax = numpy.array([0], numpy.float) mit = numpy.array([self.options['MIT'][1]], numpy.int) mfv = numpy.array([self.options['MFV'][1]], numpy.int) met = numpy.array([self.options['MET'][1]], numpy.int) mec = numpy.array([self.options['MEC'][1]], numpy.int) if (myrank == 0): if (self.options['IPRINT'][1]>=0): iprint = numpy.array([self.options['IPRINT'][1]], numpy.int) else: raise IOError('Incorrect Output Level Setting') #end else: iprint = numpy.array([0], numpy.int) #end iout = numpy.array([self.options['IOUT'][1]], numpy.int) ifile = self.options['IFILE'][1] if (iprint >= 0): if os.path.isfile(ifile): os.remove(ifile) #end #end iterm = numpy.array([0], numpy.int) nres = numpy.array([0], numpy.int) ndec = numpy.array([0], numpy.int) nrem = numpy.array([0], numpy.int) nadd = numpy.array([0], numpy.int) nit = numpy.array([0], numpy.int) nfv = numpy.array([0], numpy.int) nfg = numpy.array([0], numpy.int) nfh = numpy.array([0], numpy.int) # Storage Arrays self.stored_data = {} self.stored_data['x'] = {} #numpy.zeros([nvar],float) self.stored_data['f'] = {} #numpy.zeros([nobj],float) self.stored_data['g'] = {} #numpy.zeros([ncon],float) self.stored_data['df'] = {} #numpy.zeros([nvar],float) self.stored_data['dg'] = {} #numpy.zeros([ncon,nvar],float) # Run PSQP t0 = time.time() psqp.psqpw(nf,nc,xx,xi,xl,xu,gg,gi,gl,gu,cz,xmax,tolx,tolc,tolg, rpf,cmax,ff,mit,mfv,met,mec,iprint,iout,ifile,iterm,nres, ndec,nrem,nadd,nit,nfv,nfg,nfh,pobj,pdobj,pcon,pdcon) sol_time = time.time() - t0 if (myrank == 0): if self.sto_hst: log_file.close() if tmp_file: hos_file.close() name = hos_file.filename os.remove(name+'.cue') os.remove(name+'.bin') os.rename(name+'_tmp.cue',name+'.cue') os.rename(name+'_tmp.bin',name+'.bin') #end #end #end if (iprint > 0): psqp.closeunit(self.options['IOUT'][1]) #end # Store Results sol_inform = {} sol_inform['value'] = iterm[0] sol_inform['text'] = self.getInform(iterm[0]) if store_sol: sol_name = 'PSQP Solution to ' + opt_problem.name sol_options = copy.copy(self.options) if sol_options.has_key('defaults'): del sol_options['defaults'] #end sol_evals = nfv[0] + nfg[0]*nvar sol_vars = copy.deepcopy(opt_problem._variables) i = 0 for key in sol_vars.keys(): sol_vars[key].value = xx[i] i += 1 #end sol_objs = copy.deepcopy(opt_problem._objectives) i = 0 for key in sol_objs.keys(): sol_objs[key].value = ff[i] i += 1 #end if ncon > 0: sol_cons = copy.deepcopy(opt_problem._constraints) i = 0 for key in sol_cons.keys(): sol_cons[key].value = gg[i] i += 1 #end else: sol_cons = {} #end if ncon > 0: sol_lambda = numpy.zeros(ncon,float) for i in xrange(ncon): sol_lambda[i] = cz[i] #end else: sol_lambda = {} #end opt_problem.addSol(self.__class__.__name__, sol_name, objfunc, sol_time, sol_evals, sol_inform, sol_vars, sol_objs, sol_cons, sol_options, display_opts=disp_opts, Lambda=sol_lambda, Sensitivities=sens_type, myrank=myrank, arguments=args, **kwargs) #end return ff, xx, sol_inform def _on_setOption(self, name, value): ''' Set Optimizer Option Value (Optimizer Specific Routine) Documentation last updated: November. 30, 2010 - Ruben E. Perez ''' pass def _on_getOption(self, name): ''' Get Optimizer Option Value (Optimizer Specific Routine) Documentation last updated: November. 30, 2010 - Ruben E. Perez ''' pass def _on_getInform(self, infocode): ''' Get Optimizer Result Information (Optimizer Specific Routine) Keyword arguments: ----------------- id -> STRING: Option Name Documentation last updated: November. 30, 2010 - Ruben E. Perez ''' return self.informs[infocode] def _on_flushFiles(self): ''' Flush the Output Files (Optimizer Specific Routine) Documentation last updated: November. 30, 2010 - Ruben E. Perez ''' # iprint = self.options['IPRINT'][1] if (iprint > 0): psqp.pyflush(self.options['IOUT'][1]) #end #============================================================================== # PSQP Optimizer Test #============================================================================== if __name__ == '__main__': # Test PSQP print 'Testing ...' psqp = PSQP() print psqp