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Microsoft Visual C++ 14.0 or greater is required in Windows

Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/

Optimized Experimental

To use the optimized codes,create virtual environment, install all the dependencies and run the build.bat ( for Windows ) [DELETED, use python script]UPD 02.08.2024 build.py ( universal ) file from the optimized directory. The script will automatically build all the C libraries for you. All the codes will be available in this directory.

Alternatively, you can download the release from GitHub.

Overview optimiezed

Still in development. (Last Update), build and use the optimized codes in the optimized directory. (Modules ready to use Ready)

The names of the functions and classes in the NON OPTIMIZED PYTHON VERSION are different from their counterparts after the build in the optimized version. Stay tuned for a table of content that includes all of them. For now, you can look at the __init__.py files in the respective directories and modules. For modules you can look at optimized/lib_name/module_name/Module/*.py file

also you can import raw functions from the optimized/lib_name/module_name/build directory. For functions names look at optimized/lib_name/module_name/*.pyx file.

Example:

Python Interface:

Raw import:

from optimized.lineq.matrix_methods.build import determinant

( all functions names also in optimized/lib_name/module_name/Module/*.py file). Example - lineq.MatrixMethods.funcs line 1:18

from ..build.matrix_methods import (
    determinant,
    sum_matrices_wrapper,
    multiply_matrix_by_scalar_wrapper,
    multiply_matrices_wrapper,
    sig,
    absolute,
    random,
    max_matrix,
    inv,
    LU,
    cholv1,
    cholv2,
    eigen,
    power_method,
    norm,
    vec_approx,
)

Functions names are determinant, sum_matrices_wrapper, multiply_matrix_by_scalar_wrapper, multiply_matrices_wrapper, sig, absolute, random, max_matrix, inv, LU, cholv1, cholv2, eigen, power_method, norm, vec_approx.

Table of Content (OPTIMIZED)

Still in development. (Last Update), build and use the optimized codes in the optimized directory.

Ready:

Api

Version - API-v0.1A2

To run api local host use run_api.py script. ( I will add CLI for API usage in future CLI-API)
Check usage instructions in API-DATA or just run run_api.py and check home page.

Current Api Support:

Current version of API supports the following functions:

optimized.lineq.matrix_methods.python_interface

function args description use_pattern example
determinant type: type of input
matrix: matrix		 Returns determinant of matrix link_to_api/det/?type=type&matrix=matrix ( list or str ). example_str: link_to_api/det/?matrix=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/det/?type=list&matrix=[[1,2,3],[4,5,6],[7,8,9]]
sum_matrices type: type of input
matrix1: matrix1
matrix2: matrix2		 Returns sum of matrices link_to_api/sum_m/?type=type&matrix1=matrix1&matrix2=matrix2 ( list or str ) example_str: link_to_api/sum_m/?matrix1=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/sum_m/?type=list&matrix1=[[2,2,3],[4,5,5],[7,1.2,7]]&matrix2=[[1,2,3],[7,4,6],[7,1.2,7]]
multiply_matrix_by_scalar type: type of input
matrix: matrix
double: scalar		 Returns multiplied matrix link_to_api/mult_m_s/?type=type&matrix=matrix1&double=double ( list or str for matrix, int or float for double ) example_str: link_to_api/mult_m_s/?matrix=1 2 3\n4 5 6\n7 8 9&double=1.23,
example_list: link_to_api/mult_m_s/?type=list&matrix=[[2,2,3],[4,5,5],[7,1.2,7]]&double=1.23
multiply_matrices type: type of input
matrix1: matrix1
matrix2: matrix2		 Returns multiplied matrix link_to_api/mult_m/?type=type&matrix1=matrix1&matrix2=matrix2 ( list or str ) example_str: link_to_api/mult_m/?matrix1=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/mult_m/?type=list&matrix1=[[2,2,3],[4,5,5],[7,1.2,7]]&matrix2=[[1,2,3],[7,4,6],[7,1.2,7]]
sig double: x Returns -1 if x < 0, 0 if x == 0, 1 if x > 0 link_to_api/sig/?double=double example: link_to_api/sig/?double=1.23
absolute double: x Returns absolute value of number link_to_api/abs/?double=double example: link_to_api/abs/?double=1.23
random dmin: min
dmax: max		 Returns random number in range (min, max) link_to_api/rand/?dmin=dmin&dmax=dmax example: link_to_api/rand/?dmin=1.23&dmax=1.43
max_matrix type: type of input
matrix: matrix		 Returns max value in matrix link_to_api/max_m/?type=type&matrix=matrix ( list or str ) example_str: link_to_api/max_m/?matrix=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/max_m/?type=list&matrix=[[2,2,3],[4,5,5],[7,1.2,7]]
inverse type: type of input
matrix: matrix		 Returns inverted matrix link_to_api/inv/?type=type&matrix=matrix ( list or str ) example_str: link_to_api/inv/?matrix=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/inv/?type=list&matrix=[[2,2,3],[4,5,5],[7,1.2,7]]
LU type: type of input
matrix: matrix		 Returns LU decomposition link_to_api/lu/?type=type&matrix=matrix ( list or str ) example_str: link_to_api/lu/?matrix=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/lu/?type=list&matrix=[[2,2,3],[4,5,5],[7,1.2,7]]
cholv1 type: type of input
matrix: matrix		 Returns cholesky decomposition link_to_api/cholv1/?type=type&matrix=matrix ( list or str ) example_str: link_to_api/cholv1/?matrix=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/cholv1/?type=list&matrix=[[2,2,3],[4,5,5],[7,1.2,7]]
cholv2 type: type of input
matrix: matrix		 Returns cholesky decomposition link_to_api/cholv2/?type=type&matrix=matrix ( list or str ) example_str: link_to_api/cholv2/?matrix=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/cholv2/?type=list&matrix=[[2,2,3],[4,5,5],[7,1.2,7]]
eigen type: type of input
matrix: matrix
itern: number
tol: tolerance		 Returns eigenvalues and eigenvectors (max, min) <-> max = (max, maxv), min = (min, minv) link_to_api/eig_mm/?type=type&matrix=matrix1&itern=itern&tol=double ( list or str for matrix, int or float for double ) example_str: link_to_api/eig_mm/?matrix=1 2 3\n4 5 6\n7 8 9&itern=10&tol=0.01
norm type: type of input
vector: vector		 Returns norm of vector link_to_api/norm/?type=type&vector=vector ( list or str for vector) example_str: link_to_api/norm/?vector=1 2 3,
example_list: link_to_api/norm/?type=list&vector=[1,2,3]
vec_approx(vector_a, vector_b) type: type of input
vector1: vector
vector2: vector		 Returns vector approximation ( True or False ) link_to_api/approx/?type=type&vector1=vector1&vector2=vector2 ( list or str for vector) example_str: link_to_api/approx/?vector1=1 2 3&vector2=4 5 6
example_list: link_to_api/approx/?type=list&vector1=[1,2,3]&vector2=[4,5,6]

optimized.lineq.generator.python_interface

function args description use_pattern example
generate_matrix size: size of matrix (NxN)
rng: range of random of elements (a_ij in [-rng, rng]
mode: mode of generating -> 3diag, symm, default		 Returns generated matrix link_to_api/rand_m/?size=size&rng=rng&mode=mode example_str: link_to_api/rand_m/?size=25&rng=1.132&mode=3diag
example_list: link_to_api/rand_m/?size=25&rng=1.13&mode=3diag
generate_vector size: size of vector Returns generated vector link_to_api/rand_v/?size=size example_str: link_to_api/rand_v/?size=5
example_list: link_to_api/rand_v/?size=5

optimized.lineq.checker.python_interface

function args description use_pattern example
diagonal_domination type: type of input
matrix: matrix		 Return result of checker link_to_api/chk_dd/?type=type&matrix=matrix ( list or str ) example_str: link_to_api/chk_dd/?matrix=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/chk_dd/?type=list&matrix=[[1,2,3],[4,5,6],[7,8,9]]
symmetric_check type: type of input
matrix: matrix		 Return result of checker link_to_api/chk_sy/?type=type&matrix=matrix ( list or str ) example_str: link_to_api/chk_sy/?matrix=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/chk_sy/?type=list&matrix=[[1,2,3],[4,5,6],[7,8,9]]
sylvesters_criterion type: type of input
matrix: matrix		 Return result of checker link_to_api/chk_pd/?type=type&matrix=matrix ( list or str ) example_str: link_to_api/chk_pd/?matrix=1 2 3\n4 5 6\n7 8 9,
example_list: link_to_api/chk_pd/?type=list&matrix=[[1,2,3],[4,5,6],[7,8,9]]

CLI-API:

still in development