Metadata-Version: 1.0
Name: pymop
Version: 0.1.1
Summary: Optimization Test Problems
Home-page: https://github.com/julesy89/pymop
Author: Julian Blank
Author-email: blankjul@egr.msu.edu
License: MIT
Description: pymop - Optimization Test Problems
        ==================================
        
        Installation
        ------------
        
        The test problems are uploaded to the PyPi Repository.
        
        .. code:: bash
        
            pip install pymop
        
        Usage
        -----
        
        .. code:: python
        
            # numpy arrays are required as an input
            import numpy as np
        
            # first import the specific problem to be solved
            from pymop.dtlz import DTLZ1
        
            # initialize it with the necessary parameters
            problem = DTLZ1(n_var=10, n_obj=3)
        
            # evaluation function returns by default two numpy arrays - objective function values and constraints -
            # as input either provide a vector
            F, G = problem.evaluate(np.random.random(10))
        
            # or a whole matrix to evaluate several solutions at once
            F, G = problem.evaluate(np.random.random((100, 10)))
        
            # if no constraints should be returned
            F = problem.evaluate(np.random.random((100, 10)), return_constraints=0)
        
            # if only the constraint violation should be returned - vector of zeros if no constraints exist
            from pymop.welded_beam import WeldedBeam
        
            problem = WeldedBeam()
            F, CV = problem.evaluate(np.random.random((100, 4)), return_constraints=2)
        
        Problems
        --------
        
        In this package single- as well as multi-objective test problems are
        included.
        
        -  Single-Objective:
        
           -  Ackley
           -  BNH
           -  Griewank
           -  Knapsack
           -  Schwefel
           -  Sphere
           -  Zakharov
        
        -  Multi-Objective:
        
           -  DTLZ 1-7
           -  ZDT 1-6
           -  Carside Impact
           -  BNH
           -  Kursawe
           -  OSY
           -  TNK
           -  Welded Beam
        
        Implementation
        --------------
        
        All problems are implemented to efficiently evaluate multiple input
        points at a time. Therefore, the input can be a n x m dimensional
        matrix, where n is the number of points to evaluate and m the number of
        variables.
        
        Contact
        -------
        
        Feel free to contact me if you have any question: blankjul@egr.msu.edu
        
Keywords: optimization
Platform: UNKNOWN
