Source code for pyomo.contrib.pynumero.linalg.ma57

#  ___________________________________________________________________________
#
#  Pyomo: Python Optimization Modeling Objects
#  Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
#  Under the terms of Contract DE-NA0003525 with National Technology and
#  Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
#  rights in this software.
#  This software is distributed under the 3-clause BSD License.
#  ___________________________________________________________________________

from pyomo.common.fileutils import find_library
from pyomo.contrib.pynumero.linalg.utils import (validate_index,
        validate_value, _NotSet)
import numpy.ctypeslib as npct
import numpy as np
import ctypes 
import os

[docs]class MA57Interface(object): libname = _NotSet
[docs] @classmethod def available(cls): if cls.libname is _NotSet: cls.libname = find_library('pynumero_MA57') if cls.libname is None: return False return os.path.exists(cls.libname)
def __init__(self, work_factor=None, fact_factor=None, ifact_factor=None): if not MA57Interface.available(): raise RuntimeError( 'Could not find pynumero_MA57 library.') self.work_factor = work_factor self.fact_factor = fact_factor self.ifact_factor = ifact_factor self.lib = ctypes.cdll.LoadLibrary(self.libname) array_1d_double = npct.ndpointer(dtype=np.double, ndim=1, flags='CONTIGUOUS') array_2d_double = npct.ndpointer(dtype=np.double, ndim=2, flags='CONTIGUOUS') array_1d_int = npct.ndpointer(dtype=np.intc, ndim=1, flags='CONTIGUOUS') # Declare arg and res types of functions: # Do I need to specify that this function takes no argument? self.lib.new_MA57_struct.restype = ctypes.c_void_p # return type is pointer to MA57_struct. Why do I use c_void_p here? self.lib.free_MA57_struct.argtypes = [ctypes.c_void_p] self.lib.set_icntl.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int] # Do I need to specify that this function returns nothing? self.lib.get_icntl.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.get_icntl.restype = ctypes.c_int self.lib.set_cntl.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_double] self.lib.get_cntl.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.get_cntl.restype = ctypes.c_double self.lib.get_info.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.get_info.restype = ctypes.c_int self.lib.get_rinfo.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.get_rinfo.restype = ctypes.c_double self.lib.alloc_keep.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.alloc_work.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.alloc_fact.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.alloc_ifact.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.set_nrhs.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.set_lrhs.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.set_job.argtypes = [ctypes.c_void_p, ctypes.c_int] self.lib.do_symbolic_factorization.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int, array_1d_int, array_1d_int] self.lib.do_numeric_factorization.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int, array_1d_double] self.lib.do_backsolve.argtypes = [ctypes.c_void_p, ctypes.c_int, array_2d_double] self.lib.do_iterative_refinement.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int, array_1d_double, array_1d_int, array_1d_int, array_1d_double, array_1d_double, array_1d_double] self.lib.do_reallocation.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_double, ctypes.c_int] self.icntl_len = 20 self.cntl_len = 5 self.info_len = 40 self.rinfo_len = 20 self._ma57 = self.lib.new_MA57_struct() def __del__(self): self.lib.free_MA57_struct(self._ma57)
[docs] def set_icntl(self, i, val): validate_index(i, self.icntl_len, 'ICNTL') validate_value(i, int, 'ICNTL') # NOTE: Use the FORTRAN indexing (same as documentation) to # set and access info/cntl arrays from Python, whereas C # functions use C indexing. Maybe this is too confusing. self.lib.set_icntl(self._ma57, i-1, val)
[docs] def get_icntl(self, i): validate_index(i, self.icntl_len, 'ICNTL') return self.lib.get_icntl(self._ma57, i-1)
[docs] def set_cntl(self, i, val): validate_index(i, self.cntl_len, 'CNTL') validate_value(val, float, 'CNTL') self.lib.set_cntl(self._ma57, i-1, val)
[docs] def get_cntl(self, i): validate_index(i, self.cntl_len, 'CNTL') return self.lib.get_cntl(self._ma57, i-1)
[docs] def get_info(self, i): validate_index(i, self.info_len, 'INFO') return self.lib.get_info(self._ma57, i-1)
[docs] def get_rinfo(self, i): validate_index(i, self.rinfo_len, 'RINFO') return self.lib.get_info(self._ma57, i-1)
[docs] def do_symbolic_factorization(self, dim, irn, jcn): irn = irn.astype(np.intc, casting='safe', copy=True) jcn = jcn.astype(np.intc, casting='safe', copy=True) # TODO: maybe allow user the option to specify size of KEEP ne = irn.size self.ne_cached = ne self.dim_cached = dim assert ne == jcn.size, 'Dimension mismatch in row and column arrays' self.lib.do_symbolic_factorization(self._ma57, dim, ne, irn, jcn) return self.get_info(1)
[docs] def do_numeric_factorization(self, dim, entries): entries = entries.astype(np.float64, casting='safe', copy=True) ne = entries.size assert ne == self.ne_cached,\ ('Wrong number of entries in matrix. Please re-run symbolic' 'factorization with correct nonzero coordinates.') assert dim == self.dim_cached,\ ('Dimension mismatch between symbolic and numeric factorization.' 'Please re-run symbolic factorization with the correct ' 'dimension.') if self.fact_factor is not None: min_size = self.get_info(9) self.lib.alloc_fact(self._ma57, int(self.fact_factor*min_size)) if self.ifact_factor is not None: min_size = self.get_info(10) self.lib.alloc_ifact(self._ma57, int(self.ifact_factor*min_size)) self.lib.do_numeric_factorization(self._ma57, dim, ne, entries) return self.get_info(1)
[docs] def do_backsolve(self, rhs): rhs = rhs.astype(np.double, casting='safe', copy=True) shape = rhs.shape if len(shape) == 1: rhs_dim = rhs.size nrhs = 1 rhs = np.array([rhs]) elif len(shape) == 2: # FIXME raise NotImplementedError( 'Funcionality for solving a matrix of right hand ' 'is buggy and needs fixing.') rhs_dim = rhs.shape[0] nrhs = rhs.shape[1] else: raise ValueError( 'Right hand side must be a one or two-dimensional array') # This does not necessarily need to be true; each RHS could have length # larger than N (for some reason). In the C interface, however, I assume # that LRHS == N assert self.dim_cached == rhs_dim, 'Dimension mismatch in RHS' # TODO: Option to specify a JOB other than 1. By my understanding, # different JOBs allow partial factorizations to be performed. # Currently not supported - unclear if it should be. if nrhs > 1: self.lib.set_nrhs(self._ma57, nrhs) if self.work_factor is not None: self.lib.alloc_work(self._ma57, int(self.work_factor*nrhs*rhs_dim)) self.lib.do_backsolve(self._ma57, rhs_dim, rhs) if len(shape) == 1: # If the user input rhs as a 1D array, return the solution # as a 1D array. rhs = rhs[0, :] return rhs