Metadata-Version: 2.1
Name: oppy
Version: 0.0.6
Summary: An optimization package in python
Home-page: https://gitlab.inf.uni-konstanz.de/ag-volkwein/oppy
Author: AG Volkwein
Maintainer-email: agvolkwein.oppy@uni-konstanz.de
License: BSD-3
Description: # oppy
        ## Optimization Package in Python
        
        Documentation is available in the docstrings and
        online [here](https://www.mathematik.uni-konstanz.de/en/volkwein/python/oppy/).
        
        The idea behind oppy was to provide some optimization methods which are used
        in the group of Prof. Dr. Volkwein quite often. After a while oppy
        grew up to a whole optimization package.
        
        Besides algorithms for solving constrained, unconstrained and non-linear
        optimization problems, the package contains built-in iterative methods for
        solving linear systems.
        
        Advanced methods for optimization are included such as SQP (Square Quadratic
        Programming), Augmented Lagrangian and different newton-type methods.
        Furthermore certain Krylov methods are implemented for solving linear
        systems in a stable way.
        
        The goal is to provide a straightforward integration of the library to other
        applications such that other methods benefit from it.
        
        The package is still in develop mode. If you want to install oppy use
        
            pip install git+https://gitlab.inf.uni-konstanz.de/ag-volkwein/oppy
        
        For access, further questions, remarks and ideas please contact
        us <agvolkwein.oppy@uni-konstanz.de>. See also the website 
        [here](https://www.mathematik.uni-konstanz.de/en/volkwein/python/oppy/).
        
        ## Available subpackages
        
        ### conOpt
        Subpackage which provide some methods for constraint optimization. For
        problems which are subject to equality and inequality constraints like
        
            min f(x)
            s.t. e(x) = 0
            g(x) <= 0
        
        we can use
        
        * Penalty Method
        * Augmented Lagrangian Method
        * SQP with a BFGS update strategy (at the moment only equality constraint)
        
        and for box constraint problems like
        
            min f(x) 
            s. t. x_a <= x <= x_b
        
        we can use
        
        * Projected gradient Method
        * The L-BFGS-B Method
        * Projected Newton-Krylov Method (if you can provide the
           action of the second derivative)
        
        ### itMet
        Iterative methods for solving linear systems like
        
            Ax = b.
        
        Here we can use either stationary methods like
        
        * Jacobi
        * GauÃŸ-Seidel
        * SOR
        
        or we use krylov methods like
        
        * steepest descent
        * CG
        * GMRES
        
        For future release we are planing to add preconditioning in the
        krylov methods. There of course you will be able to use the
        stationary methods as precondition method.
        
        ### linOpt
        Linear optimization methods. With the methods in this subpackage we can either
        solve linear least-squares problem like
        
            min ||Ax - b||_2
        
        or we solve linear programming
        
            max  c^T x
            s. t. Ax <= b
            x <= 0
        
        with or without integer constraints. For that kind of problems we have
        the following methods:
        
        * linear least square
        * simplex
        * branch and bound
        
        ### multOpt
        Scalarization methods for solving (possibly box-constrained) multiobjective
        optimization problems of the form
        
            min (f_1(x), ..., f_k(x)),
            s.t. x_a <= x <= x_b.
        
        The general idea of scalarization methods is to transform the
        multiobjective optimization problem into a series of scalar optimization
        problems. which can then be solved by using methods from unconstrained or
        constrained optimization (see the subpackages unconOpt or conOpt). Here we
        can use the following three scalarization methods
        
        * Weighted-Sum Method (WSM)
        * Euclidean Reference Point Method (ERPM)
        * Pascoletti-Serafini Method (PSM)
        
        ### options
        This subpackage contains the options class for all methods use in oppy.
        
        ### results
        This subpackage contains the class for the returns which oppy use.
        
        ### tests
        Unittests of oppy.
        
        ### unconOpt
        Subpackage which provide some methods for unconstrained optimization, e.g:
        
            min f(x)
        
        Right now we can solve this kind of problems with line search based first-
        and second-order methods.
        
        * Line Search Methods
            * Armijo
            * Wolfe-Powell
        * Optimization Methods
            * Gradient Method
            * Newton Method
            * Nonlinear CG (with different strategies like Fletcher-Reves)
            * Quasi-Newton Methods (with different strategies like
               BFGS, Broyden, DFP, ...)
        
        ### visualization
        Some methods for visualization.
        
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