An optimization package in python
Optimization Package in Python
Documentation is available in the docstrings and online here.
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 email@example.com. See also the website here.
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)
Iterative methods for solving linear systems like
Ax = b.
Here we can use either stationary methods like
or we use krylov methods like
- steepest descent
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.
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
- branch and bound
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)
This subpackage contains the options class for all methods use in oppy.
This subpackage contains the class for the returns which oppy use.
Unittests of oppy.
Subpackage which provide some methods for unconstrained optimization, e.g:
Right now we can solve this kind of problems with line search based first- and second-order methods.
- Line Search Methods
- Optimization Methods
- Gradient Method
- Newton Method
- Nonlinear CG (with different strategies like Fletcher-Reves)
- Quasi-Newton Methods (with different strategies like BFGS, Broyden, DFP, ...)
Some methods for visualization.
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