Metadata-Version: 2.1
Name: tensorflow-constrained-optimization
Version: 0.1
Summary: A library for performing constrained optimization in TensorFlow
Home-page: https://github.com/google-research/tensorflow_constrained_optimization
Author: Google Inc.
Author-email: no-reply@google.com
License: Apache 2.0
Keywords: tensorflow machine learning optimizer constraint rate
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: six
Provides-Extra: tensorflow
Requires-Dist: tensorflow (<2) ; extra == 'tensorflow'
Provides-Extra: tensorflow-gpu
Requires-Dist: tensorflow-gpu (<2) ; extra == 'tensorflow-gpu'

*TensorFlow Constrained Optimization* (TFCO) is a library for optimizing
inequality-constrained problems in TensorFlow.

In the most general case, both the objective function and the constraints are
represented as `Tensor`s, giving users the maximum amount of flexibility in
specifying their optimization problems. Constructing these `Tensor`s can be
cumbersome, so we also provide helper functions to make it easy to construct
constrained optimization problems based on *rates*, i.e. proportions of the
training data on which some event occurs (e.g. the error rate, true positive
rate, recall, etc).


