#!/usr/local/bin/python ''' pyNLPQL - A Python pyOpt interface to NLPQL. Copyright (c) 2008-2011 by pyOpt Developers All rights reserved. Revision: 1.5 $Date: 21/06/2010 21:00$ Tested on: --------- Linux with g77 Linux with gfortran Linux with pathf95 Win32 with g77 Mac with g95 Developers: ----------- - Dr. Ruben E. Perez (RP) - Mr. C.A.(Sandy) Mader (SM) - Mr. Peter Jansen (PJ) History ------- v. 1.0 - Initial Class Creation (RP, 2007) v. 1.1 - Wrapper (f2py) Fixes (SM, 2007) - Interface Fixes (RP, 2007) v. 1.2 - Migrate to pyOpt Framework (RP, 2008) - Bug fix for CS (PJ, 2008) v. 1.3 - User-Provided Sensitivities Support (PJ,RP, 2008) v. 1.4 - History support (PJ,RP, 2010) v. 1.5 - Gradient Class Support (PJ,RP, 2010) ''' __version__ = '$Revision: $' ''' To Do: - ''' # ============================================================================= # NLPQL Library # ============================================================================= try: import nlpql except: raise ImportError('NLPQL 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) # ============================================================================= # NLPQL Optimizer Class # ============================================================================= class NLPQL(Optimizer): ''' NLPQL Optimizer Class - Inherited from Optimizer Abstract Class ''' def __init__(self, pll_type=None, *args, **kwargs): ''' NLPQL 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 = 'NLPQL' category = 'Local Optimizer' def_opts = { # NLPQL Options 'Accurancy':[float,1e-6], # Convergence Accurancy 'ScaleBound':[float,1e30], # 'maxFun':[int,20], # Maximum Number of Function Calls During Line Search 'maxIt':[int,500], # Maximum Number of Iterations 'iPrint':[int,2], # Output Level (0 - None, 1 - Final, 2 - Major, 3 - Major/Minor, 4 - Full) 'mode':[int,0], # NLPQL Mode (0 - Normal Execution, 1 to 18 - See Manual) 'iout':[int,6], # Output Unit Number 'lmerit':[bool,True], # Merit Function Type (True - L2 Augmented Penalty, False - L1 Penalty) 'lql':[bool,False], # QP Subproblem Solver (True - Quasi-Newton, False - Cholesky) 'iFile':[str,'NLPQL.out'], # Output File Name } informs = { -2 : 'Compute gradient values w.r.t. the variables stored in' \ ' first column of X, and store them in DF and DG.' \ ' Only derivatives for active constraints ACTIVE(J)=.TRUE. need to be computed.', -1 : 'Compute objective fn and all constraint values subject' \ 'the variables found in the first L columns of X, and store them in F and G.', 0 : 'The optimality conditions are satisfied.', 1 : ' The algorithm has been stopped after MAXIT iterations.', 2 : ' The algorithm computed an uphill search direction.', 3 : ' Underflow occurred when determining a new approximation matrix' \ 'for the Hessian of the Lagrangian.', 4 : 'The line search could not be terminated successfully.', 5 : 'Length of a working array is too short.' \ ' More detailed error information is obtained with IPRINT>0', 6 : 'There are false dimensions, for example M>MMAX, N>=NMAX, or MNN2<>M+N+N+2.', 7 : 'The search direction is close to zero, but the current iterate is still infeasible.', 8 : 'The starting point violates a lower or upper bound.', 9 : 'Wrong input parameter, i.e., MODE, LDL decomposition in D and C' \ ' (in case of MODE=1), IPRINT, IOUT', 10 : 'Internal inconsistency of the quadratic subproblem, division by zero.', 100 : 'The solution of the quadratic programming subproblem has been' \ ' terminated with an error message and IFAIL is set to IFQL+100,' \ ' where IFQL denotes the index of an inconsistent constraint.', } Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs) def __solve__(self, opt_problem={}, sens_type='FD', store_sol=True, disp_opts=False, store_hst=False, hot_start=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 - Peter W. Jansen ''' # if ((self.poa) and (sens_mode.lower() == 'pgc')): raise NotImplementedError("pyNLPQL - 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 'pyNLPQL: 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) #====================================================================== # NLPQL - Objective/Constraint Values Function (Real Valued) #====================================================================== def nlfunc(m,me,mmax,n,f,g,x,active): # 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 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: [ff,gg,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: [ff,gg,fail] = Bcast([ff,gg,fail],root=0) elif not self.h_start: [ff,gg,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(ff,'obj') log_file.write(gg,'con') log_file.write(fail,'fail') #end #end # Objective Assigment if isinstance(ff,complex): f = ff.astype(float) else: f = ff #end # Constraints Assigment (negative gg as nlpql uses g(x) >= 0) for i in xrange(len(opt_problem._constraints.keys())): if isinstance(gg[i],complex): g[i] = -gg[i].astype(float) else: g[i] = -gg[i] #end #end return f,g #====================================================================== # NLPQL - Objective/Constraint Gradients Function #====================================================================== def nlgrad(m,me,mmax,n,f,g,df,dg,x,active,wa): 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[0:len(opt_problem._constraints.keys())], *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 # Gradient Assignment for i in xrange(len(opt_problem._variables.keys())): df[i] = dff[0,i] for j in xrange(len(opt_problem._constraints.keys())): dg[j,i] = -dgg[j,i] #end #end return df,dg # Variables Handling nvar = len(opt_problem._variables.keys()) xl = [] xu = [] xx = [] for key in opt_problem._variables.keys(): if (opt_problem._variables[key].type == 'c'): xl.append(opt_problem._variables[key].lower) xu.append(opt_problem._variables[key].upper) xx.append(opt_problem._variables[key].value) elif (opt_problem._variables[key].type == 'i'): raise IOError('NLPQL cannot handle integer design variables') elif (opt_problem._variables[key].type == 'd'): raise IOError('NLPQL cannot handle discrete design variables') #end #end xl = numpy.array(xl) xu = numpy.array(xu) 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()) neqc = 0 #gg = [] if ncon > 0: for key in opt_problem._constraints.keys(): if opt_problem._constraints[key].type == 'e': neqc += 1 #end #gg.append(opt_problem._constraints[key].value) #end #end #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 mm = numpy.array([ncon], numpy.int) me = numpy.array([neqc], numpy.int) mmx = 200 if (ncon >= mmx): mmx = ncon + 1 #end mmax = numpy.array([mmx], numpy.int) nn = numpy.array([nvar], numpy.int) nmx = 200 if (nvar >= nmx): nmx = nvar + 1 #end nmax = numpy.array([nmx], numpy.int) mnn2 = numpy.array([mm[0]+nn[0]+nn[0]+2], numpy.int) #xx = _ConcatenateVector(self.variables, 'value') #ff = self.objective.value gg = numpy.zeros([mmax], numpy.float) df = numpy.zeros([nmax], numpy.float) dg = numpy.zeros([mmax,nmax], numpy.float) uu = numpy.zeros([mnn2], numpy.float) #xl = _ConcatenateVector(self.variables, 'lower') #xu = _ConcatenateVector(self.variables, 'upper') cc = numpy.zeros([nmax,nmax], numpy.float) dd = numpy.zeros([nmax], numpy.float) acc = numpy.array([self.options['Accurancy'][1]], numpy.float) scbou = numpy.array([self.options['ScaleBound'][1]], numpy.float) maxfun = numpy.array([self.options['maxFun'][1]], numpy.int) maxit = numpy.array([self.options['maxIt'][1]], numpy.int) if (myrank == 0): if (self.options['iPrint'][1]>=0 and self.options['iPrint'][1]<=4): 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 if (self.options['mode'][1]>=0 and self.options['mode'][1]<=18): mode = self.options['mode'][1] else: raise IOError('Incorrect Mode Setting') #end iout = self.options['iout'][1] ifile = self.options['iFile'][1] if (iprint > 0): if os.path.isfile(ifile): os.remove(ifile) #end #end ifail = numpy.array([0], numpy.int) lwa0 = 100000 lwa1 = 4*mmax + 4*ncon + 19*nvar + 55 lwa2 = mmax*nvar + 4*mmax + 4*ncon + 18*nvar + 55 lwa3 = 3/2*(nvar + 1)*(nvar + 1) + 10*nvar + 2*ncon + 10 lwaM = max([lwa0,lwa1,lwa2,lwa3]) + lwa3 lwa = numpy.array([lwaM], numpy.int) wa = numpy.zeros([lwa], numpy.float) lkwa = numpy.array([mmx+2*nmx+20], numpy.int) kwa = numpy.zeros([lkwa], numpy.intc) lactiv = numpy.array([2*mmx+15], numpy.int) active = numpy.zeros([lactiv], numpy.bool) lmerit = numpy.array([self.options['lmerit'][1]], numpy.bool) lql = numpy.array([self.options['lql'][1]], numpy.bool) fmp = numpy.array([eps], numpy.float) # Run NLPQL t0 = time.time() nlpql.nlpql1(mm,me,mmax,nn,nmax,mnn2,xx,ff,gg,df,dg,uu,xl,xu,cc, dd,acc,scbou,maxfun,maxit,iprint,mode,iout,ifile,ifail, wa,lwa,kwa,lkwa,active,lactiv,lmerit,lql,fmp,nlfunc,nlgrad) 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): nlpql.closeunit(self.options['iout'][1]) #end # Store Results sol_inform = {} sol_inform['value'] = ifail[0] sol_inform['text'] = self.getInform(ifail[0]) if store_sol: sol_name = 'NLPQL Solution to ' + opt_problem.name sol_options = copy.copy(self.options) if sol_options.has_key('defaults'): del sol_options['defaults'] #end sol_evals = kwa[0] 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] = uu[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: May. 07, 2008 - Ruben E. Perez ''' pass def _on_getOption(self, name): ''' Get Optimizer Option Value (Optimizer Specific Routine) Documentation last updated: May. 07, 2008 - 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: May. 07, 2008 - Ruben E. Perez ''' # if (infocode <= 10): return self.informs[infocode] else: return self.informs[100] #end def _on_flushFiles(self): ''' Flush the Output Files (Optimizer Specific Routine) Documentation last updated: August. 09, 2009 - Ruben E. Perez ''' # iPrint = self.options['iPrint'][1] if (iPrint > 0): nlpql.pyflush(self.options['iout'][1]) #end #============================================================================== # NLPQL Optimizer Test #============================================================================== if __name__ == '__main__': # Test NLPQL print 'Testing ...' nlpql = NLPQL() print nlpql