1
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I decided to implement a solver for linear systems of equations based on the gaussian elimination and reduction to upper triangular form. The gaussian elimination is quite simple to implement but the function to go from a "gauss-eliminated" matrix (upper triangular form) to a solution is excessively complicated in my opinion (solve_given_augmented_gauss_eliminated), I am interested in both improving the readability of this function and in improving the performance that as of now is about 100x times slower than the np.linalg implementation. Probably trying to avoid pure python for loops and using vectorialized operations would improve performance but I am not sure how to proceed.

import numpy as np
import doctest
import time

def gauss_elimination(matrix):
    """
    Performs gaussian elimination.
    Example from: http://sites.science.oregonstate.edu/math/home/programs/undergrad/CalculusQuestStudyGuides/vcalc/gauss/gauss.html
    >>> gauss_elimination(np.array([[1,-3,1,4],\
                                    [2,-8,8,-2],\
                                    [-6,3,-15,9]], dtype=float))
    array([[  1.,  -3.,   1.,   4.],
           [  0.,  -2.,   6., -10.],
           [  0.,   0., -54., 108.]])
    """
    for i in range(len(matrix)):
        for j in range(i):
            weight_j = matrix[i][j] / matrix[j][j]
            matrix[i] -= (matrix[j] * weight_j)
    return matrix
    
def solve_given_augmented_gauss_eliminated(gauss_eliminated):
    """
    The augmented matrix is the matrix of the coefficients of the system with
    the known terms vector added to the right, for example:
        
    1x + 0y = 1
    1x + 1y = 3
    
    is represented by:
        
    >>> m = np.array([[1, 0, 1],\
                      [1, 1, 3]], dtype=float)
        
    The gaussian eliminated form is upper triangular and is:
        
    1x + 0y = 1
    0x + 1y = 2
    
    So
    
    >>> m = np.array([[1, 0, 1],\
                      [0, 1, 2]], dtype=float)
    >>> solve_given_augmented_gauss_eliminated(m.copy())
    array([1., 2.])
    """
    row_len = len(gauss_eliminated[0])
    # start with ones, so unknown variables make no difference when multiplied
    values = np.ones(len(gauss_eliminated))
    for i in range(len(gauss_eliminated)-1, -1, -1):
        # substitute the values of the known variables
        gauss_eliminated[i][:len(values)] = gauss_eliminated[i][:len(values)] * values
        # the numerator is the element on the left minus the sum of the elements on the right
        numerator = gauss_eliminated[i][row_len-1] - sum(gauss_eliminated[i][i+1:row_len-1])
        # determine the value of the i-th element of the solution vector
        values[i] = numerator / gauss_eliminated[i][i]
    return values

def check_solution(aug_gauss_eliminated, sol):
    """
    >>> m = np.array([[1, 0, 1],\
                      [1, 1, 3]], dtype=float)
    >>> check_solution(m, np.array([1,2]))
    True
    """
    return all( (sum(row[:len(sol)] * sol) == row[-1]) for row in aug_gauss_eliminated)

def solve(aug_matrix):
    """
    Example from https://www.cliffsnotes.com/study-guides/algebra/linear-algebra/linear-systems/gaussian-elimination
    examples 1 and 3
    
    Be careful, the input matrix will be changed in a weird way, use .copy() to be safe.
    
    >>> m1 = np.array([[1, 1, 3],\
                       [3, -2, 4]], dtype=float)
    >>> solve(m1.copy())
    array([2., 1.])
    >>> check_solution(m1, _)
    True

    >>> m3 = np.array([[1, -2,  1,  0],\
                        [2,  1, -3,  5],\
                        [4, -7,  1, -1]], dtype=float)
    >>> solve(m3.copy())
    array([3., 2., 1.])
    >>> check_solution(m3, _)
    True
    """
    gauss_eliminated = gauss_elimination(aug_matrix)
    return solve_given_augmented_gauss_eliminated(gauss_eliminated)

if __name__ == "__main__":
    doctest.testmod()
    for size in [10, 100, 200, 500, 1000]:
        coefficients =  np.random.rand(size, size)
        known = np.random.rand(size)
        random_system = np.hstack((coefficients,known.reshape(size,1)))
        
        start = time.time()
        sol1 = solve(random_system)
        print("Size: ", size)
        print(time.time() - start)
        
        start = time.time()
        sol2 = np.linalg.solve(coefficients, known)
        print(time.time() - start)
        
        # Final check, does my code give the same result as numpy?
        print(np.allclose(sol1, sol2))
        print()

I also run the cProfile utility on my code finding out that the gauss_elimination is taking the longest time because it is a quadratic algorithm while solve_given_augmented_gauss_eliminated is linear, if my big O analysis is correct.

$>python -m cProfile -s tottime gauss_elim.py
Size:  10
0.0005228519439697266
0.0
True

Size:  100
0.012234210968017578
0.0023505687713623047
True

Size:  200
0.052938222885131836
0.003468036651611328
True

Size:  500
0.3867616653442383
0.00789952278137207
True

Size:  1000
1.7185072898864746
0.015315055847167969
True

         127075 function calls (124279 primitive calls) in 2.382 seconds

   Ordered by: internal time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        5    2.113    0.423    2.113    0.423 gauss_elim.py:5(gauss_elimination)
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        5    0.006    0.001    0.057    0.011 gauss_elim.py:22(solve_given_augmented_gauss_eliminated)
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    1    0.000    0.000    0.000    0.000 mixins.py:59(NDArrayOperatorsMixin)
   14    0.000    0.000    0.000    0.000 re.py:223(split)
    1    0.000    0.000    0.000    0.000 hermite_e.py:1(<module>)
    1    0.000    0.000    0.000    0.000 core.py:6439(__new__)
    1    0.000    0.000    0.000    0.000 legendre.py:1(<module>)
   83    0.000    0.000    0.000    0.000 {method 'get' of 'mappingproxy' objects}
    4    0.000    0.000    0.000    0.000 ntpath.py:77(join)
   50    0.000    0.000    0.000    0.000 {built-in method sys.intern}
    1    0.000    0.000    0.000    0.000 __config__.py:3(<module>)
    1    0.000    0.000    0.000    0.000 _globals.py:1(<module>)
   59    0.000    0.000    0.000    0.000 {built-in method builtins.repr}
   24    0.000    0.000    0.000    0.000 numerictypes.py:515(_scalar_type_key)
    5    0.000    0.000    0.000    0.000 shape_base.py:23(atleast_1d)
    1    0.000    0.000    0.006    0.006 defmatrix.py:1(<module>)
    6    0.000    0.000    0.003    0.001 <__array_function__ internals>:2(concatenate)
    1    0.000    0.000    0.000    0.000 histograms.py:1(<module>)
   15    0.000    0.000    0.000    0.000 doctest.py:678(get_examples)
    5    0.000    0.000    0.000    0.000 {method 'astype' of 'numpy.ndarray' objects}
   61    0.000    0.000    0.000    0.000 inspect.py:159(isfunction)
   49    0.000    0.000    0.000    0.000 _string_helpers.py:16(english_lower)
    1    0.000    0.000    0.000    0.000 loader.py:1(<module>)
    1    0.000    0.000    0.000    0.000 einsumfunc.py:1(<module>)
    1    0.000    0.000    0.009    0.009 subprocess.py:377(check_output)
   11    0.000    0.000    0.000    0.000 {built-in method _abc._abc_register}
    4    0.000    0.000    0.000    0.000 warnings.py:181(_add_filter)
   10    0.000    0.000    0.000    0.000 linalg.py:107(_makearray)
    1    0.000    0.000    0.000    0.000 _iotools.py:451(StringConverter)
    3    0.000    0.000    0.001    0.000 __init__.py:340(__init__)
    1    0.000    0.000    0.000    0.000 os.py:48(<listcomp>)
   50    0.000    0.000    0.000    0.000 __init__.py:419(<genexpr>)
   15    0.000    0.000    0.000    0.000 bdb.py:273(_set_stopinfo)
    2    0.000    0.000    0.000    0.000 {built-in method builtins.sorted}
    1    0.000    0.000    0.000    0.000 tokenize.py:388(open)
    7    0.000    0.000    0.000    0.000 core.py:2553(_arraymethod)
   52    0.000    0.000    0.000    0.000 {method 'setdefault' of 'dict' objects}
   91    0.000    0.000    0.000    0.000 {built-in method _sre.unicode_iscased}
    2    0.000    0.000    0.000    0.000 {method 'readline' of '_io.BufferedReader' objects}
    1    0.000    0.000    0.000    0.000 _exceptions.py:1(<module>)
   14    0.000    0.000    0.000    0.000 doctest.py:947(_from_module)
   68    0.000    0.000    0.000    0.000 {method 'isidentifier' of 'str' objects}
   36    0.000    0.000    0.000    0.000 getlimits.py:39(<lambda>)
    1    0.000    0.000    0.000    0.000 {method 'dot' of 'numpy.ndarray' objects}
   15    0.000    0.000    0.000    0.000 cmd.py:76(__init__)
    1    0.000    0.000    0.001    0.001 scanner.py:1(<module>)
   13    0.000    0.000    0.000    0.000 _dtype_ctypes.py:71(_from_ctypes_scalar)
    1    0.000    0.000    0.000    0.000 helper.py:1(<module>)
    3    0.000    0.000    0.000    0.000 decoder.py:332(decode)
   32    0.000    0.000    0.000    0.000 _type_aliases.py:46(<genexpr>)
    9    0.000    0.000    0.000    0.000 _ufunc_config.py:434(__exit__)
    1    0.000    0.000    0.000    0.000 {built-in method _hashlib.openssl_md5}
    3    0.000    0.000    0.000    0.000 warnings.py:130(filterwarnings)
    1    0.000    0.000    0.000    0.000 _string_helpers.py:1(<module>)
    1    0.000    0.000    0.000    0.000 _type_aliases.py:130(_add_integer_aliases)
    1    0.000    0.000    0.001    0.001 runner.py:1(<module>)
    5    0.000    0.000    0.003    0.001 <__array_function__ internals>:2(hstack)
    1    0.000    0.000    0.000    0.000 memmap.py:1(<module>)
    1    0.000    0.000    0.000    0.000 ufunclike.py:1(<module>)
   38    0.000    0.000    0.000    0.000 {built-in method numpy.geterrobj}
   48    0.000    0.000    0.000    0.000 inspect.py:81(ismethod)
    1    0.000    0.000    0.000    0.000 {built-in method nt.open}
   13    0.000    0.000    0.000    0.000 mixins.py:44(_numeric_methods)
    1    0.000    0.000    0.000    0.000 defchararray.py:1805(chararray)
   30    0.000    0.000    0.000    0.000 enum.py:438(<genexpr>)
    2    0.000    0.000    0.000    0.000 subprocess.py:1462(_wait)
    5    0.000    0.000    0.000    0.000 sre_parse.py:861(_parse_flags)
    1    0.000    0.000    0.002    0.002 main.py:1(<module>)
    1    0.000    0.000    0.000    0.000 pprint.py:11(<module>)
   20    0.000    0.000    0.000    0.000 {built-in method time.time}
   63    0.000    0.000    0.000    0.000 abc.py:7(abstractmethod)
    1    0.000    0.000    0.000    0.000 polynomial.py:1068(poly1d)
    1    0.000    0.000    0.000    0.000 arraypad.py:1(<module>)
    1    0.000    0.000    0.000    0.000 pdb.py:138(Pdb)
   33    0.000    0.000    0.000    0.000 {method 'clear' of 'dict' objects}
    1    0.000    0.000    0.000    0.000 bdb.py:1(<module>)
   14    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap_external>:159(_path_isdir)
    1    0.000    0.000    0.000    0.000 contextlib.py:489(__exit__)
    6    0.000    0.000    0.000    0.000 sre_compile.py:432(_generate_overlap_table)
    1    0.000    0.000    0.000    0.000 _datasource.py:1(<module>)
   50    0.000    0.000    0.000    0.000 {method '__contains__' of 'frozenset' objects}
    1    0.000    0.000    0.000    0.000 pathlib.py:120(_WindowsFlavour)
    1    0.000    0.000    0.000    0.000 subprocess.py:1240(_get_handles)
    1    0.000    0.000    0.000    0.000 format.py:1(<module>)
    1    0.000    0.000    0.002    0.002 doctest.py:579(DocTestParser)
   10    0.000    0.000    0.001    0.000 extras.py:235(__init__)
    1    0.000    0.000    0.000    0.000 _globals.py:77(__new__)
    1    0.000    0.000    0.001    0.001 fnmatch.py:1(<module>)
    5    0.000    0.000    0.000    0.000 <__array_function__ internals>:2(result_type)
    5    0.000    0.000    0.000    0.000 <__array_function__ internals>:2(isclose)
    1    0.000    0.000    0.000    0.000 _iotools.py:1(<module>)
    2    0.000    0.000    0.000    0.000 core.py:2948(_update_from)
    1    0.000    0.000    0.001    0.001 string.py:1(<module>)
   18    0.000    0.000    0.000    0.000 {method 'translate' of 'bytearray' objects}
    2    0.000    0.000    0.000    0.000 genericpath.py:39(isdir)
   46    0.000    0.000    0.000    0.000 enum.py:792(value)
    1    0.000    0.000    0.000    0.000 numbers.py:32(Complex)
    8    0.000    0.000    0.000    0.000 core.py:8098(__init__)
    1    0.000    0.000    0.000    0.000 linalg.py:73(_determine_error_states)
  4/3    0.000    0.000    0.000    0.000 {method 'view' of 'numpy.ndarray' objects}
   25    0.000    0.000    0.000    0.000 {built-in method numpy.asanyarray}
   16    0.000    0.000    0.000    0.000 {built-in method builtins.any}
   86    0.000    0.000    0.000    0.000 _compat_pickle.py:167(<genexpr>)
    1    0.000    0.000    0.000    0.000 random.py:101(Random)
    1    0.000    0.000    0.000    0.000 traceback.py:1(<module>)
    1    0.000    0.000    0.000    0.000 _asarray.py:1(<module>)
    1    0.000    0.000    0.000    0.000 platform.py:233(_norm_version)
    1    0.000    0.000    0.002    0.002 doctest.py:845(find)
    1    0.000    0.000    0.000    0.000 util.py:1(<module>)
    6    0.000    0.000    0.000    0.000 enum.py:561(_check_for_existing_members)
   48    0.000    0.000    0.000    0.000 {method 'pop' of 'list' objects}
   13    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:765(exec_module)
   16    0.000    0.000    0.000    0.000 {method 'copy' of 'dict' objects}
    1    0.000    0.000    0.001    0.001 result.py:1(<module>)
    1    0.000    0.000    0.000    0.000 suite.py:1(<module>)
   15    0.000    0.000    0.000    0.000 {method 'finditer' of 're.Pattern' objects}
    2    0.000    0.000    0.000    0.000 sre_compile.py:416(_bytes_to_codes)
    1    0.000    0.000    0.000    0.000 datetime.py:1245(time)
    1    0.000    0.000    0.000    0.000 _type_aliases.py:211(_set_array_types)
    1    0.000    0.000    0.000    0.000 {function Random.seed at 0x0000019E2E70E280}
    5    0.000    0.000    0.000    0.000 _collections_abc.py:760(get)
    1    0.000    0.000    0.000    0.000 datetime.py:1594(datetime)
   15    0.000    0.000    0.000    0.000 fromnumeric.py:70(<dictcomp>)
    1    0.000    0.000    0.000    0.000 core.py:2817(__new__)
    1    0.000    0.000    0.001    0.001 string.py:69(__init_subclass__)
    1    0.000    0.000    0.000    0.000 threading.py:795(__init__)
   20    0.000    0.000    0.000    0.000 mixins.py:16(_binary_method)
    5    0.000    0.000    0.000    0.000 {method 'reshape' of 'numpy.ndarray' objects}
   71    0.000    0.000    0.000    0.000 {built-in method _sre.unicode_tolower}
   60    0.000    0.000    0.000    0.000 {built-in method builtins.divmod}
    1    0.000    0.000    0.000    0.000 cp1252.py:22(decode)
    1    0.000    0.000    0.000    0.000 contextlib.py:76(inner)
    6    0.000    0.000    0.000    0.000 enum.py:81(__init__)
    1    0.000    0.000    0.000    0.000 linecache.py:80(updatecache)
    1    0.000    0.000    0.000    0.000 _type_aliases.py:158(_set_up_aliases)
    1    0.000    0.000    0.001    0.001 code.py:1(<module>)
    1    0.000    0.000    0.000    0.000 numbers.py:4(<module>)
   17    0.000    0.000    0.000    0.000 <string>:1(<lambda>)
    1    0.000    0.000    0.000    0.000 _polybase.py:1(<module>)
   18    0.000    0.000    0.000    0.000 ntpath.py:34(_get_bothseps)
    2    0.000    0.000    0.000    0.000 functools.py:478(lru_cache)
    1    0.000    0.000    0.002    0.002 glob.py:1(<module>)
    1    0.000    0.000    0.000    0.000 struct.py:1(<module>)
   16    0.000    0.000    0.000    0.000 _add_newdocs_scalars.py:18(type_aliases_gen)
    1    0.000    0.000    0.000    0.000 threading.py:1307(__init__)
    6    0.000    0.000    0.000    0.000 enum.py:197(<dictcomp>)
   41    0.000    0.000    0.000    0.000 enum.py:551(<lambda>)

I cut the output to stay within the characther limit.

\$\endgroup\$

0

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