Initial commit

.gitignore

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+*.pyc
+
+.installed.cfg
+bin
+develop-eggs
+
+*.egg-info
+
+tmp
+build
+dist

.hgignore

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+syntax: glob
+
+.installed.cfg
+bin
+develop-eggs
+
+*.egg-info
+
+tmp
+build
+dist

HACKING.txt

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+Development setup
+=================
+
+To create a buildout,
+
+  $ python bootstrap.py
+  $ bin/buildout
+
+Release HOWTO
+=============
+
+To make a release, 
+
+  1) Update release date/version in NEWS.txt and setup.py
+  2) Run 'python setup.py sdist'
+  3) Test the generated source distribution in dist/
+  4) Upload to PyPI: 'python setup.py sdist register upload'
+  5) Increase version in setup.py (for next release)
+

MANIFEST.in

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+include README.md
+include NEWS.txt

NEWS.txt

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+News
+====
+
+0.1
+---
+
+*Release date: 05-Out-2017*
+
+* Alpha 0.1 released.

README.md

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+# PSIFA
+
+**PSIFA** (**P**attern **S**earch and **I**mplicit **F**iltering **A**lgorithm) is a derivative-free optimization algorithm developed by Ferreira, Ehrhardt and Santos (2017) that has been designed for linearly constrained problems with noise in the objective function. It combines some elements of the pattern search approach of Lewis and Torczon (2000) with ideas from the implicit filtering method of Kelley (2011).
+
+<!-- ## Installation
+
+Binary installers for the latest released version will be available at the Python Package Index.
+
+`pip install psifa` -->
+
+## Requirements
+
+- numpy (>= 1.13)
+- scipy (>= 0.19)
+
+## Example of use
+
+```python
+from psifa.api import psifa
+import numpy as np
+import pprint
+
+# Objective function.
+def f(x):
+    y = (1. / (27. * np.sqrt(3.))) * ((x[0] - 3.)**2 - 9.) * x[1]**3
+    eval_success = True
+    return y, eval_success
+
+# Problem constraints.
+A = np.array([
+    [1. / np.sqrt(3.), -1.],
+    [1., np.sqrt(3.)],
+    [-1., -np.sqrt(3.)]
+])
+l = np.array([0., 0., -6.])
+u = np.array([np.inf, np.inf, np.inf])
+bl = np.array([0., 0.])
+bu = np.array([np.inf, np.inf])
+n = 2
+
+# Initial guess for the solution.
+x0 = np.array([1., 0.5])
+
+# Find a global optimum using PSIFA.
+x, y, iterations, alpha, evaluations, history, exit_cause = \
+    psifa(f, x0, A, l, u, n, bl, bu)
+
+# Show the results.
+print('Solution: x = {}'.format(x))
+print('Function value: f(x) = {}'.format(y))
+print('Step length: alpha = {}'.format(alpha))
+print('Number of iterations: {}'.format(iterations))
+print('Function evaluations: {}'.format(evaluations))
+print('Exit cause: {}'.format(exit_cause))
+print('History:')
+pprint.PrettyPrinter(indent=2).pprint(history)
+```
+
+## License
+
+[GNU GPLv3](LICENSE)

bootstrap.py

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+##############################################################################
+#
+# Copyright (c) 2006 Zope Corporation and Contributors.
+# All Rights Reserved.
+#
+# This software is subject to the provisions of the Zope Public License,
+# Version 2.1 (ZPL).  A copy of the ZPL should accompany this distribution.
+# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
+# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
+# FOR A PARTICULAR PURPOSE.
+#
+##############################################################################
+"""Bootstrap a buildout-based project
+
+Simply run this script in a directory containing a buildout.cfg.
+The script accepts buildout command-line options, so you can
+use the -c option to specify an alternate configuration file.
+
+$Id: bootstrap.py 102545 2009-08-06 14:49:47Z chrisw $
+"""
+
+import os, shutil, sys, tempfile, urllib2
+from optparse import OptionParser
+
+tmpeggs = tempfile.mkdtemp()
+
+is_jython = sys.platform.startswith('java')
+
+# parsing arguments
+parser = OptionParser()
+parser.add_option("-v", "--version", dest="version",
+                          help="use a specific zc.buildout version")
+parser.add_option("-d", "--distribute",
+                   action="store_true", dest="distribute", default=True,
+                   help="Use Disribute rather than Setuptools.")
+
+options, args = parser.parse_args()
+
+if options.version is not None:
+    VERSION = '==%s' % options.version
+else:
+    VERSION = ''
+
+USE_DISTRIBUTE = options.distribute
+args = args + ['bootstrap']
+
+to_reload = False
+try:
+    import pkg_resources
+    if not hasattr(pkg_resources, '_distribute'):
+        to_reload = True
+        raise ImportError
+except ImportError:
+    ez = {}
+    if USE_DISTRIBUTE:
+        exec urllib2.urlopen('http://python-distribute.org/distribute_setup.py'
+                         ).read() in ez
+        ez['use_setuptools'](to_dir=tmpeggs, download_delay=0, no_fake=True)
+    else:
+        exec urllib2.urlopen('http://peak.telecommunity.com/dist/ez_setup.py'
+                             ).read() in ez
+        ez['use_setuptools'](to_dir=tmpeggs, download_delay=0)
+
+    if to_reload:
+        reload(pkg_resources)
+    else:
+        import pkg_resources
+
+if sys.platform == 'win32':
+    def quote(c):
+        if ' ' in c:
+            return '"%s"' % c # work around spawn lamosity on windows
+        else:
+            return c
+else:
+    def quote (c):
+        return c
+
+cmd = 'from setuptools.command.easy_install import main; main()'
+ws  = pkg_resources.working_set
+
+if USE_DISTRIBUTE:
+    requirement = 'distribute'
+else:
+    requirement = 'setuptools'
+
+if is_jython:
+    import subprocess
+
+    assert subprocess.Popen([sys.executable] + ['-c', quote(cmd), '-mqNxd',
+           quote(tmpeggs), 'zc.buildout' + VERSION],
+           env=dict(os.environ,
+               PYTHONPATH=
+               ws.find(pkg_resources.Requirement.parse(requirement)).location
+               ),
+           ).wait() == 0
+
+else:
+    assert os.spawnle(
+        os.P_WAIT, sys.executable, quote (sys.executable),
+        '-c', quote (cmd), '-mqNxd', quote (tmpeggs), 'zc.buildout' + VERSION,
+        dict(os.environ,
+            PYTHONPATH=
+            ws.find(pkg_resources.Requirement.parse(requirement)).location
+            ),
+        ) == 0
+
+ws.add_entry(tmpeggs)
+ws.require('zc.buildout' + VERSION)
+import zc.buildout.buildout
+zc.buildout.buildout.main(args)
+shutil.rmtree(tmpeggs)

buildout.cfg

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+[buildout]
+parts = python scripts
+develop = .
+eggs = PSIFA
+
+[python]
+recipe = zc.recipe.egg
+interpreter = python
+eggs = ${buildout:eggs}
+
+[scripts]
+recipe = zc.recipe.egg:scripts
+eggs = ${buildout:eggs}

setup.py

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+from setuptools import setup, find_packages
+import sys, os
+
+here = os.path.abspath(os.path.dirname(__file__))
+README = open(os.path.join(here, 'README.md')).read()
+NEWS = open(os.path.join(here, 'NEWS.txt')).read()
+
+
+version = '0.1'
+
+install_requires = [
+    # List your project dependencies here.
+    # For more details, see:
+    # http://packages.python.org/distribute/setuptools.html#declaring-dependencies
+    'numpy',
+    'scipy',
+]
+
+
+setup(name='PSIFA',
+    version=version,
+    description="Python implementation of the Pattern Search Implicit Filtering Algorithm (PSIFA)",
+    long_description=README + '\n\n' + NEWS,
+    classifiers=[
+      # Get strings from http://pypi.python.org/pypi?%3Aaction=list_classifiers
+      'Development Status :: 3 - Alpha',
+      'Intended Audience :: Science/Research',
+      'Programming Language :: Python :: 2.7',
+      'Programming Language :: Python :: 3.6',
+      'Topic :: Scientific/Engineering :: Mathematics',
+    ],
+    keywords='derivative-free-optimization linearly-constrained-minimization noisy-optimization global-convergence degenerate-constraints',
+    author='Carlos H. Villa Pinto',
+    author_email='[email protected]',
+    url='https://github.com/chvillap',
+    license='GNU GPLv3',
+    packages=find_packages('src'),
+    package_dir = {'': 'src'},
+    include_package_data=True,
+    zip_safe=False,
+    install_requires=install_requires,
+    entry_points={
+        'console_scripts':
+            ['PSIFA=psifa:main']
+    }
+)

src/psifa/__init__.py

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+__doc__ = """
+PSIFA (Pattern Search and Implicit Filtering Algorithm) is a derivative-free
+optimization algorithm that has been designed for linearly constrained problems
+with noise in the objective function. It combines some elements of the pattern
+search approach of Lewis and Torczon (2000) with ideas from the implicit
+filtering method of Kelley (2011).
+"""