Main
~~Python is an open-source, versatile, interpreted high-level programming language that is supported on Windows, Linux, MacOS, etc. Python is extended by a number of add-on packages for optimization, numeric analysis, and graphical analysis. The following packages are recommended for Windows users and include Python 2.7, Numpy 1.6.2, Matplotlib 1.2.0, Scipy 0.11.0, and Pyserial 2.5.~~

* [[Attach:python-2.7.3.msi | Python 2.7 for Windows]]

* [[Attach:numpy-1.6.2.exe | Numpy 1.6.2 for Windows]]

* [[Attach:matplotlib-1.2.0.exe | Matplotlib 1.2.0 for Windows]]

* [[Attach:scipy-0.11.0.exe | Scipy 0.11.0 for Windows]]

* [[Attach:pyserial-2.5.exe | Pyserial 2.5 for Windows]]

* [[Attach:pywin32-218.exe | Pywin32 218 for Windows]]

Any additional packages must be compatible with Python 2.7 (32-bit version). The 32-bit version is recommended because official releases of Numpy and Scipy packages are only available in that format. There are many optimization packages that have been developed for Python with a few of them listed below:

* [[http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00-introduction-to-computer-science-and-programming-fall-2008/assignments/ | MIT Open Course Ware Tutorial on Python and IDLE]]

!!!! Tutorial on MATLAB and Python

[[Attach:nlp_matlab_python_tutorial.zip | Download MATLAB and Python Tutorial Files (nlp_matlab_python_tutorial.zip)]]

(:html:)

<iframe width="560" height="315" src="http://www.youtube.com/embed/t84YMw8p34w?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

[[Attach:nlp_matlab_python_tutorial.zip | Download Tutorial Files]]

[[Attach:nlp_matlab_python_tutorial.zip | Download Tutorial Files]]
~~A few optimization software packages are listed~~ below. Assignments and projects can be completed with any of these software ~~packages~~.

----

!!! Python

Python is an open-source, versatile, interpreted high-level programming language that is supported on Windows, Linux, MacOS, etc. Python is extended by a number of add-on packages for optimization, numeric analysis, and graphical analysis. The following packages are recommended for Windows users and include Python 2.7, Numpy 1.6.2, and Matplotlib 1.2.0.

* [[Attach:python-2.7.3.msi | Python 2.7 for Windows]]

* [[Attach:numpy-1.6.2.exe | Numpy 1.6.2 for Windows]]

* [[Attach:matplotlib-1.2.0.exe | Matplotlib 1.2.0 for Windows]]

Any additional packages must be compatible with Python 2.7 (32-bit version). The 32-bit version is recommended because official releases of Numpy and Scipy packages are only available in that format. There are many optimization packages that have been developed for Python with a few of them listed below:

* [[https://software.sandia.gov/trac/coopr/wiki/Documentation/RelatedProjects | List of Python Optimization Packages]]

* [[http://www.scipy.org/ | Scientific Computing with SciPy]]

This course uses the [[http://apmonitor.com/wiki/index.php/Main/PythonApp | APM Python package]] for tutorials and class instruction. The APMonitor Modeling Language is integrated with Python through a set of functions that allow optimization problems to be solved as a web-service.

* [[http://en.wikipedia.org/wiki/Python_(programming_language) | Wikipedia: Python]]

!!! MATLAB

MATLAB allows mathematical operations, plotting, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Fortran, and Java. A number of optimization tools are available in the [[http://www.mathworks.com/products/optimization/ | Optimization Toolbox]]. An additional package, Simulink, adds graphical simulation and design for dynamic systems. MATLAB users come from engineering, science, and economics in both academic research and industrial applications.

## Optimization Course Software

## Main.OptimizationTools History

Hide minor edits - Show changes to output

Changed lines 43-51 from:

* [[Attach:python-2.7.3.msi | Python 2.7 for Windows]]

* [[Attach:numpy-1.6.2.exe | Numpy 1.6.2 for Windows]]

* [[Attach:matplotlib-1.2.0.exe | Matplotlib 1.2.0 for Windows]]

* [[Attach:scipy-0.11.0.exe | Scipy 0.11.0 for Windows]]

* [[Attach:pyserial-2.5.exe | Pyserial 2.5 for Windows]]

* [[Attach:pywin32-218.exe | Pywin32 218 for Windows]]

Any additional packages must be compatible with Python 2.7 (32-bit version). The 32-bit version is recommended because official releases of Numpy and Scipy packages are only available in that format. There are many optimization packages that have been developed for Python with a few of them listed below:

to:

Python is a high-level and general-purpose programming language and is a [[https://www.google.com/search?q=popular+programming+languages|top choice for programmers (Google search)]]. Part of the reason that it is a popular choice for scientists and engineers is the language versatility, online community of users, and powerful analysis packages such as Numpy and Scipy. This course uses Python 2.7 or Python 3.6+. Python is free and open-source and is easy to install with [[https://www.continuum.io/downloads|Anaconda (IPython, Jupyter, Spyder)]], [[https://www.jetbrains.com/pycharm/download/|PyCharm]], or the [[https://www.python.org/downloads/|Python.org]] distributions. Below is a tutorial on getting started with Python with a complete installation guide for Anaconda and Python.org.

!!!! Install Anaconda (recommended)

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/LrMOrMb8-3s" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

!!!! Install Python from Python.org

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/Ju6zw83PoKo" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

!!!! Install Packages with '''pip''' (Command Line)

Sometimes a script uses a package that is not yet installed. Once Python is installed, a package manager such as ''pip'' or ''conda'' can be used to install, remove, or update packages.

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/Z_Kxg-EYvxM" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

Below is an example on how to install [[http://apmonitor.com/wiki/index.php/Main/PythonApp|APMonitor Optimization Suite]] from the command line (start ''cmd'').

pip install APMonitor

The ''APMonitor'' package name can be replaced with any available package name such as [[http://www.numpy.org|NumPy]].

pip install numpy

!!!! Install Packages with '''pip''' (Python Script)

Packages can also be managed from a Python script by attempting to load the package with ''try''. If the import fails, the ''except'' section imports ''pip'' and installs the package.

'''Load and Optionally Install APMonitor Package'''

(:source lang=python:)

try:

from APMonitor.apm import *

except:

import pip

pip.main(['install','APMonitor'])

from APMonitor.apm import *

(:sourceend:)

'''Load and Optionally Install NumPy Package'''

(:source lang=python:)

try:

import numpy as np

except:

import pip

pip.main(['install','numpy'])

import numpy as np

(:sourceend:)

The ''APMonitor'' or ''NumPy'' package names can be replaced with any available package. The pip package manager connects to an online repository to retrieve the latest version of the package and install it. Sometimes a package is needed on a computer that isn't connected to the internet or the package isn't available through pip. Below is information on installing a package wheel (''whl'') file.

!!!! Install Package Wheels (whl) Offline

The pip package manager can also be used to install local (previously downloaded) wheel (''.whl'') files but dependencies may not be automatically installed if not connected to the internet. Below is an example wheel file installation for NumPy version 1.13.1 and SciPy version 0.19.1 for Python 3.6 with 64-bit Python.

pip install numpy-1.13.1+mkl-cp36-cp36m-win_amd64.whl

pip install scipy-0.19.1-cp36-cp36m-win_amd64.whl

Use [[http://www.lfd.uci.edu/~gohlke/pythonlibs/|Christoph Gohlke's whl files]] for Windows installations. Many of the packages depend on the [[https://www.microsoft.com/en-us/download/details.aspx?id=53587|Visual C++ 2015 Redistributable]] (x64 and x86 for CPython 3.5 and 3.6) that is available from Microsoft. After installing the Visual C++ redistributable, download and install [[http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy|NumPy]] and [[http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy|SciPy]] packages (in that order) for Python 3.6 on Windows. The downloaded wheel file names should not be changed because the wheel file name verifies compatibility with the current Python version.

There are many optimization packages that have been developed for Python with a few of them listed below:

!!!! Install Anaconda (recommended)

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/LrMOrMb8-3s" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

!!!! Install Python from Python.org

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/Ju6zw83PoKo" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

!!!! Install Packages with '''pip''' (Command Line)

Sometimes a script uses a package that is not yet installed. Once Python is installed, a package manager such as ''pip'' or ''conda'' can be used to install, remove, or update packages.

(:html:)

<iframe width="560" height="315" src="https://www.youtube.com/embed/Z_Kxg-EYvxM" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

Below is an example on how to install [[http://apmonitor.com/wiki/index.php/Main/PythonApp|APMonitor Optimization Suite]] from the command line (start ''cmd'').

pip install APMonitor

The ''APMonitor'' package name can be replaced with any available package name such as [[http://www.numpy.org|NumPy]].

pip install numpy

!!!! Install Packages with '''pip''' (Python Script)

Packages can also be managed from a Python script by attempting to load the package with ''try''. If the import fails, the ''except'' section imports ''pip'' and installs the package.

'''Load and Optionally Install APMonitor Package'''

(:source lang=python:)

try:

from APMonitor.apm import *

except:

import pip

pip.main(['install','APMonitor'])

from APMonitor.apm import *

(:sourceend:)

'''Load and Optionally Install NumPy Package'''

(:source lang=python:)

try:

import numpy as np

except:

import pip

pip.main(['install','numpy'])

import numpy as np

(:sourceend:)

The ''APMonitor'' or ''NumPy'' package names can be replaced with any available package. The pip package manager connects to an online repository to retrieve the latest version of the package and install it. Sometimes a package is needed on a computer that isn't connected to the internet or the package isn't available through pip. Below is information on installing a package wheel (''whl'') file.

!!!! Install Package Wheels (whl) Offline

The pip package manager can also be used to install local (previously downloaded) wheel (''.whl'') files but dependencies may not be automatically installed if not connected to the internet. Below is an example wheel file installation for NumPy version 1.13.1 and SciPy version 0.19.1 for Python 3.6 with 64-bit Python.

pip install numpy-1.13.1+mkl-cp36-cp36m-win_amd64.whl

pip install scipy-0.19.1-cp36-cp36m-win_amd64.whl

Use [[http://www.lfd.uci.edu/~gohlke/pythonlibs/|Christoph Gohlke's whl files]] for Windows installations. Many of the packages depend on the [[https://www.microsoft.com/en-us/download/details.aspx?id=53587|Visual C++ 2015 Redistributable]] (x64 and x86 for CPython 3.5 and 3.6) that is available from Microsoft. After installing the Visual C++ redistributable, download and install [[http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy|NumPy]] and [[http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy|SciPy]] packages (in that order) for Python 3.6 on Windows. The downloaded wheel file names should not be changed because the wheel file name verifies compatibility with the current Python version.

There are many optimization packages that have been developed for Python with a few of them listed below:

Added line 49:

* [[Attach:pywin32-218.exe | Pywin32 218 for Windows]]

Changed line 43 from:

Python is an open-source, versatile, interpreted high-level programming language that is supported on Windows, Linux, MacOS, etc. Python is extended by a number of add-on packages for optimization, numeric analysis, and graphical analysis. The following packages are recommended for Windows users and include Python 2.7, Numpy 1.6.2, ~~and ~~Matplotlib 1.2.0.

to:

Python is an open-source, versatile, interpreted high-level programming language that is supported on Windows, Linux, MacOS, etc. Python is extended by a number of add-on packages for optimization, numeric analysis, and graphical analysis. The following packages are recommended for Windows users and include Python 2.7, Numpy 1.6.2, Matplotlib 1.2.0, Scipy 0.11.0, and Pyserial 2.5.

Added lines 47-48:

* [[Attach:scipy-0.11.0.exe | Scipy 0.11.0 for Windows]]

* [[Attach:pyserial-2.5.exe | Pyserial 2.5 for Windows]]

* [[Attach:pyserial-2.5.exe | Pyserial 2.5 for Windows]]

Added lines 54-58:

Additional links:

* [[http://en.wikipedia.org/wiki/Comparison_of_integrated_development_environments#Python | Comparison of Python Integrated Development Environments (IDEs)]]

* [[http://youtu.be/vHRRiBHI3to | Python IDE with Eclipse]]

* [[http://youtu.be/pgBdFgLngkk | Python IDE with Wingware]]

* [[http://en.wikipedia.org/wiki/Comparison_of_integrated_development_environments#Python | Comparison of Python Integrated Development Environments (IDEs)]]

* [[http://youtu.be/vHRRiBHI3to | Python IDE with Eclipse]]

* [[http://youtu.be/pgBdFgLngkk | Python IDE with Wingware]]

Added lines 53-54:

* [[http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00-introduction-to-computer-science-and-programming-fall-2008/assignments/ | MIT Open Course Ware Tutorial on Python and IDLE]]

Deleted lines 5-13:

!!!! Tutorial on MATLAB and Python

[[Attach:nlp_matlab_python_tutorial.zip | Download MATLAB and Python Tutorial Files (nlp_matlab_python_tutorial.zip)]]

(:html:)

<iframe width="560" height="315" src="http://www.youtube.com/embed/t84YMw8p34w?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

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[[Attach:nlp_matlab_python_tutorial.zip | Download MATLAB and Python Tutorial Files]]

to:

[[Attach:nlp_matlab_python_tutorial.zip | Download MATLAB and Python Tutorial Files (nlp_matlab_python_tutorial.zip)]]

Added lines 9-10:

[[Attach:nlp_matlab_python_tutorial.zip | Download MATLAB and Python Tutorial Files]]

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[[Attach:nlp_matlab_python_tutorial.zip | Download Tutorial Files]]

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[[Attach:nlp_matlab_python_tutorial.zip | Download Tutorial Files]]

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----

to:

----

(:html:)

<div id="disqus_thread"></div>

<script type="text/javascript">

/* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */

var disqus_shortname = 'apmonitor'; // required: replace example with your forum shortname

/* * * DON'T EDIT BELOW THIS LINE * * */

(function() {

var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;

dsq.src = 'http://' + disqus_shortname + '.disqus.com/embed.js';

(document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);

})();

</script>

<noscript>Please enable JavaScript to view the <a href="http://disqus.com/?ref_noscript">comments powered by Disqus.</a></noscript>

<a href="http://disqus.com" class="dsq-brlink">comments powered by <span class="logo-disqus">Disqus</span></a>

(:htmlend:)

(:html:)

<div id="disqus_thread"></div>

<script type="text/javascript">

/* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */

var disqus_shortname = 'apmonitor'; // required: replace example with your forum shortname

/* * * DON'T EDIT BELOW THIS LINE * * */

(function() {

var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;

dsq.src = 'http://' + disqus_shortname + '.disqus.com/embed.js';

(document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);

})();

</script>

<noscript>Please enable JavaScript to view the <a href="http://disqus.com/?ref_noscript">comments powered by Disqus.</a></noscript>

<a href="http://disqus.com" class="dsq-brlink">comments powered by <span class="logo-disqus">Disqus</span></a>

(:htmlend:)

Changed line 15 from:

to:

Information on APMonitor, MATLAB, OptdesX, and Python is listed below. Assignments and projects can be completed with any of these software platforms.

Added lines 7-14:

!!!! Tutorial on MATLAB and Python

(:html:)

<iframe width="560" height="315" src="http://www.youtube.com/embed/t84YMw8p34w?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

!!!! Optimization Software

(:html:)

<iframe width="560" height="315" src="http://www.youtube.com/embed/t84YMw8p34w?rel=0" frameborder="0" allowfullscreen></iframe>

(:htmlend:)

!!!! Optimization Software

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!!! APMonitor

to:

!!!! APMonitor

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!!! MATLAB

to:

!!!! MATLAB

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!!! OptdesX

to:

!!!! OptdesX

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!!! Python

to:

!!!! Python

Changed line 34 from:

The OptdesX software was developed at BYU. It contains the best algorithms still in use for constrained nonlinear optimization, including the algorithms in Excel and Matlab. We use OptdesX as a learning tool and we can get inside the code and see what the algorithms are doing, which we cannot do with other software. We can also look at gradients, which are very useful in understanding scaling and in developing robust designs. OptdesX is available through CAEDM Linux workstations or through remote logon to shared servers.

to:

The OptdesX software was developed at BYU. It contains some of the best algorithms still in use for constrained nonlinear optimization, including the algorithms in Excel and Matlab. We use OptdesX as a learning tool and we can get inside the code and see what the algorithms are doing, which we cannot do with other software. We can also look at gradients, which are very useful in understanding scaling and in developing robust designs. OptdesX is available through CAEDM Linux workstations or through remote logon to shared servers.

Changed line 14 from:

* [[http://apmonitor.com/wiki/index.php/Main/SyntaxHighlighting | APM File Editor]]

to:

* [[http://apmonitor.com/wiki/index.php/Main/SyntaxHighlighting | APM Model File Editor]]

Added line 14:

* [[http://apmonitor.com/wiki/index.php/Main/SyntaxHighlighting | APM File Editor]]

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There are many software tools available to solve optimization problems ranging from free and open-source to proprietary commercial packages. ~~A few optimization software packages are listed below. Assignments and projects can ~~be ~~completed with any available~~ software packages.

to:

There are many software tools available to solve optimization problems ranging from free and open-source to proprietary commercial packages. In general, students want to chose a software platform that will be both state-of-the-art and accessible long-term. Solving optimization problems requires some familiarity with a computer programming language. Some of the [[http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html | popular programming languages (see TIOBE index)]] also include capabilities for scientific computing.

A few optimization software packages are listed below. Assignments and projects can be completed with any of these software packages.

A few optimization software packages are listed below. Assignments and projects can be completed with any of these software packages.

Deleted line 13:

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to:

* [[http://en.wikipedia.org/wiki/APMonitor | Wikipedia: APMonitor]]

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to:

* [[http://en.wikipedia.org/wiki/MATLAB | Wikipedia: MATLAB]]

Deleted line 34:

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----

!!! Python

Python is an open-source, versatile, interpreted high-level programming language that is supported on Windows, Linux, MacOS, etc. Python is extended by a number of add-on packages for optimization, numeric analysis, and graphical analysis. The following packages are recommended for Windows users and include Python 2.7, Numpy 1.6.2, and Matplotlib 1.2.0.

* [[Attach:python-2.7.3.msi | Python 2.7 for Windows]]

* [[Attach:numpy-1.6.2.exe | Numpy 1.6.2 for Windows]]

* [[Attach:matplotlib-1.2.0.exe | Matplotlib 1.2.0 for Windows]]

Any additional packages must be compatible with Python 2.7 (32-bit version). The 32-bit version is recommended because official releases of Numpy and Scipy packages are only available in that format. There are many optimization packages that have been developed for Python with a few of them listed below:

* [[https://software.sandia.gov/trac/coopr/wiki/Documentation/RelatedProjects | List of Python Optimization Packages]]

* [[http://www.scipy.org/ | Scientific Computing with SciPy]]

This course uses the [[http://apmonitor.com/wiki/index.php/Main/PythonApp | APM Python package]] for tutorials and class instruction. The APMonitor Modeling Language is integrated with Python through a set of functions that allow optimization problems to be solved as a web-service.

* [[http://en.wikipedia.org/wiki/Python_(programming_language) | Wikipedia: Python]]

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[[http://link.springer.com/article/10.1007%2Fs00158-002-0172-8?LI=true | Overview of OptdesX]]

[[Attach:OptdesX_Manual.pdf | OptdesX Manual]]

[[Attach:OptdesX_Manual.pdf | OptdesX Manual]]

to:

* [[http://link.springer.com/article/10.1007%2Fs00158-002-0172-8?LI=true | Overview of OptdesX]]

* [[Attach:OptdesX_Manual.pdf | OptdesX Manual]]

* [[Attach:OptdesX_Manual.pdf | OptdesX Manual]]

Changed lines 34-37 from:

----

to:

[[http://link.springer.com/article/10.1007%2Fs00158-002-0172-8?LI=true | Overview of OptdesX]]

[[Attach:OptdesX_Manual.pdf | OptdesX Manual]]

----

[[Attach:OptdesX_Manual.pdf | OptdesX Manual]]

----

Changed line 5 from:

There are many software tools available to solve optimization problems ranging from free and open-source to proprietary commercial packages. ~~Four~~ optimization software packages are listed below. Assignments and projects can be completed with any available software packages.

to:

There are many software tools available to solve optimization problems ranging from free and open-source to proprietary commercial packages. A few optimization software packages are listed below. Assignments and projects can be completed with any available software packages.

Changed lines 22-24 from:

# [[https://citrix.et.byu.edu/Citrix/XenApp/auth/login.aspx | BYU Citrix logon]] with a CAEDM account~~.~~

# [[http://info.et.byu.edu/index.php5?title=RGS | BYU RGS servers]]

to:

# [[https://citrix.et.byu.edu/Citrix/XenApp/auth/login.aspx | BYU Citrix logon]] with a BYU CAEDM account

# [[http://info.et.byu.edu/index.php5?title=RGS | BYU RGS servers]] with a BYU CAEDM account

# [[http://info.et.byu.edu/index.php5?title=RGS | BYU RGS servers]] with a BYU CAEDM account

Deleted line 34:

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The OptdesX software was developed at BYU. It contains the best algorithms still in use for constrained nonlinear optimization, including the algorithms in Excel and Matlab. We use OptdesX as a learning ~~tool—we~~ can get inside the code and see what the algorithms are doing, which we cannot do with other software. We can also look at gradients, which are very useful in understanding scaling and in developing robust designs. OptdesX is available through CAEDM Linux workstations or through remote logon to shared servers.

to:

The OptdesX software was developed at BYU. It contains the best algorithms still in use for constrained nonlinear optimization, including the algorithms in Excel and Matlab. We use OptdesX as a learning tool and we can get inside the code and see what the algorithms are doing, which we cannot do with other software. We can also look at gradients, which are very useful in understanding scaling and in developing robust designs. OptdesX is available through CAEDM Linux workstations or through remote logon to shared servers.

Added lines 5-8:

There are many software tools available to solve optimization problems ranging from free and open-source to proprietary commercial packages. Four optimization software packages are listed below. Assignments and projects can be completed with any available software packages.

----

----

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* [[http://apmonitor.com/wiki | APMonitor Documentation]]

* [[http://apmonitor.com/wiki/index.php/Main/MATLAB | APMonitor MATLAB Toolbox]]

* [[http://apmonitor.com/wiki/index.php/Main/PythonApp | APMonitor Python Toolbox]]

----

* [[http://apmonitor.com/wiki/index.php/Main/MATLAB | APMonitor MATLAB Toolbox]]

* [[http://apmonitor.com/wiki/index.php/Main/PythonApp | APMonitor Python Toolbox]]

----

Changed lines 19-20 from:

MATLAB allows mathematical operations, plotting, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Fortran, and Java. A number of optimization tools are available in the [[http://www.mathworks.com/products/optimization/ | Optimization Toolbox]]. An additional package, Simulink, adds graphical simulation and design for dynamic systems. MATLAB users come from engineering, science, and economics in both academic research and industrial applications.

to:

MATLAB allows mathematical operations, plotting, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Fortran, and Java. A number of optimization tools are available in the [[http://www.mathworks.com/products/optimization/ | Optimization Toolbox]]. An additional package, Simulink, adds graphical simulation and design for dynamic systems. MATLAB users come from engineering, science, and economics in both academic research and industrial applications. MATLAB is available through the following methods:

# Download MATLAB from [[http://software.byu.edu | software.byu.edu]] for installation on a personal computer

# [[https://citrix.et.byu.edu/Citrix/XenApp/auth/login.aspx | BYU Citrix logon]] with a CAEDM account.

# [[http://info.et.byu.edu/index.php5?title=RGS | BYU RGS servers]]

Please note that Citrix and RGS perform computing on shared servers with performance that can be less desirable at times of high loading. The most reliable route is to download MATLAB on a personal computer.

* [[http://www.mathworks.com/products/optimization/ | MATLAB Optimization Toolbox]]

----

# Download MATLAB from [[http://software.byu.edu | software.byu.edu]] for installation on a personal computer

# [[https://citrix.et.byu.edu/Citrix/XenApp/auth/login.aspx | BYU Citrix logon]] with a CAEDM account.

# [[http://info.et.byu.edu/index.php5?title=RGS | BYU RGS servers]]

Please note that Citrix and RGS perform computing on shared servers with performance that can be less desirable at times of high loading. The most reliable route is to download MATLAB on a personal computer.

* [[http://www.mathworks.com/products/optimization/ | MATLAB Optimization Toolbox]]

----

Changed lines 32-35 from:

The OptdesX software was developed at BYU. It contains the best algorithms still in use for constrained nonlinear optimization, including the algorithms in Excel and Matlab. We use OptdesX as a learning tool—we can get inside the code and see what the algorithms are doing, which we cannot do with other software. We can also look at gradients, which are very useful in understanding scaling and in developing robust designs.

to:

The OptdesX software was developed at BYU. It contains the best algorithms still in use for constrained nonlinear optimization, including the algorithms in Excel and Matlab. We use OptdesX as a learning tool—we can get inside the code and see what the algorithms are doing, which we cannot do with other software. We can also look at gradients, which are very useful in understanding scaling and in developing robust designs. OptdesX is available through CAEDM Linux workstations or through remote logon to shared servers.

----

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!!! MATLAB

MATLAB allows mathematical operations, plotting, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Fortran, and Java. A number of optimization tools are available in the [[http://www.mathworks.com/products/optimization/ | Optimization Toolbox]]. An additional package, Simulink, adds graphical simulation and design for dynamic systems. MATLAB users come from engineering, science, and economics in both academic research and industrial applications.

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(:title Optimization Course Software:)

(:keywords nonlinear, optimization, engineering optimization, kuhn-tucker, gradient methods, grg, interior point, active set, differential, algebraic, modeling language, university course:)

(:description Optimization software for use in engineering at Brigham Young University:)

!!! APMonitor

The APMonitor Modeling Language is optimization software for differential and algebraic equations. It is coupled with large-scale nonlinear programming solvers for data reconciliation, real-time optimization, dynamic simulation, and nonlinear predictive control. It is available as a web service through an internet browser or as an add-in package to MATLAB or Python programming languages.

!!! OptdesX

The OptdesX software was developed at BYU. It contains the best algorithms still in use for constrained nonlinear optimization, including the algorithms in Excel and Matlab. We use OptdesX as a learning tool—we can get inside the code and see what the algorithms are doing, which we cannot do with other software. We can also look at gradients, which are very useful in understanding scaling and in developing robust designs.

(:keywords nonlinear, optimization, engineering optimization, kuhn-tucker, gradient methods, grg, interior point, active set, differential, algebraic, modeling language, university course:)

(:description Optimization software for use in engineering at Brigham Young University:)

!!! APMonitor

The APMonitor Modeling Language is optimization software for differential and algebraic equations. It is coupled with large-scale nonlinear programming solvers for data reconciliation, real-time optimization, dynamic simulation, and nonlinear predictive control. It is available as a web service through an internet browser or as an add-in package to MATLAB or Python programming languages.

!!! OptdesX

The OptdesX software was developed at BYU. It contains the best algorithms still in use for constrained nonlinear optimization, including the algorithms in Excel and Matlab. We use OptdesX as a learning tool—we can get inside the code and see what the algorithms are doing, which we cannot do with other software. We can also look at gradients, which are very useful in understanding scaling and in developing robust designs.