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May 05, 2014, at 10:32 PM by 23.255.240.62 -
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Download APM Python (version 0.6.0) - Released 20 January 2014
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January 20, 2014, at 08:30 AM by 23.255.228.67 -
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Download APM Python (version 0.5.8d) - Released 25 March 2013
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Download APM Python (version 0.6.0) - Released 20 January 2014
June 27, 2013, at 05:08 PM by 128.187.97.18 -
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Solve this problem problem from a web-browser interface.

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  • Solve this optimization problem from a web-browser interface or download the Python source above. The Python files are contained in folder example_hs71.
June 27, 2013, at 05:06 PM by 128.187.97.18 -
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Hock-Schittkowsky Test Suite #71

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Solve this problem problem from a web-browser interface.

March 25, 2013, at 10:43 AM by 69.169.188.188 -
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Download APM Python (version 0.5.8) - Released 7 January 2013
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Download APM Python (version 0.5.8d) - Released 25 March 2013
January 17, 2013, at 12:21 AM by 69.169.188.188 -
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January 12, 2013, at 07:05 AM by 69.169.188.188 -
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(:html:) <iframe width="560" height="315" src="http://www.youtube.com/embed/t84YMw8p34w?rel=0" frameborder="0" allowfullscreen></iframe> (:htmlend:)

January 12, 2013, at 07:00 AM by 69.169.188.188 -
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Python offers a powerful scripting capabilities for solving nonlinear optimization problems. The optimization problem is sent to the APMonitor server and results are returned to the Python script. A web-interface to the solution helps to visualize the dynamic optimization problems. Example applications of nonlinear models with differential and algebraic equations are available for download below.
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APM Python is designed for large-scale optimization and accesses solvers of constrained, unconstrained, continuous, and discrete problems. Problems in linear programming, quadratic programming, integer programming, nonlinear optimization, systems of dynamic nonlinear equations, and multiobjective optimization can be solved. The platform can find optimal solutions, perform tradeoff analyses, balance multiple design alternatives, and incorporate optimization methods into external modeling and analysis software. It is free for academic and commercial use.
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January 07, 2013, at 06:01 AM by 69.169.188.188 -
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Download APM Python (version 0.5.8) - Released 1 January 2013
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Download APM Python (version 0.5.8) - Released 7 January 2013
January 07, 2013, at 06:01 AM by 69.169.188.188 -
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Download APM Python (version 0.5.7) - Released 7 March 2012
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Download APM Python (version 0.5.8) - Released 1 January 2013
May 23, 2012, at 05:17 PM by 128.187.97.18 -
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Download APM Python (version 0.5.6) - Released 7 March 2012
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Download APM Python (version 0.5.7) - Released 7 March 2012
March 07, 2012, at 11:58 AM by 128.187.97.23 -
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Download APM Python (version 0.5.6) - Released 15 Feb 2012
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Download APM Python (version 0.5.6) - Released 7 March 2012
February 15, 2012, at 08:33 PM by 69.169.188.228 -
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Download APM Python (version 0.5.6) - Released 15 Feb 2012
December 24, 2011, at 09:34 PM by 69.169.188.228 -
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(:description Use APMonitor with the power of Python scripting language:)

Python for APMonitor

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(:description APM Python: A comprehensive modeling and nonlinear optimization solution with Python scripting language:)

APM Python

December 21, 2011, at 11:20 PM by 69.169.188.228 -
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December 21, 2011, at 11:16 PM by 69.169.188.228 -
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The latest APM Python libraries are attached below. Functionality has been tested with Python 2.7 and requires only the base Python installation. Example applications that use the apm.py library are listed further down on this page.

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The latest APM Python libraries are attached below. Functionality has been tested with Python 2.7. Example applications that use the apm.py library are listed further down on this page.

December 21, 2011, at 11:15 PM by 69.169.188.228 -
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The development roadmap for this and other libraries are detailed in the release notes section. The zipped archive contains the APM Python library apm.py and a number of example problems in separate folders. Descriptions of the example problems are provided below.

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The development roadmap for this and other libraries are detailed in the release notes. The zipped archive contains the APM Python library apm.py and a number of example problems in separate folders. Descriptions of the example problems are provided below.

December 21, 2011, at 11:14 PM by 69.169.188.228 -
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The product roadmap for this and other libraries are detailed in the release notes section. The zipped archive contains the APM Python library apm.py and a number of example problems in separate folders. Descriptions of the example problems are provided below.

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The development roadmap for this and other libraries are detailed in the release notes section. The zipped archive contains the APM Python library apm.py and a number of example problems in separate folders. Descriptions of the example problems are provided below.

December 21, 2011, at 11:10 PM by 69.169.188.228 -
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December 21, 2011, at 09:03 PM by 69.169.188.228 -
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Folder example_hs071: Nonlinear Programming with Python

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Example_hs071: Nonlinear Programming with Python

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Folder example_nlc: Nonlinear Control with Python

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Example_nlc: Nonlinear Control with Python

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Folder example_tank_mhe/nlc: Nonlinear Estimation and Control with Python

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Example_tank_mhe/nlc: Nonlinear Estimation and Control with Python

December 21, 2011, at 09:02 PM by 69.169.188.228 -
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Folder example_tank_mhe: Nonlinear Estimation and Control with Python

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Folder example_tank_mhe/nlc: Nonlinear Estimation and Control with Python

December 21, 2011, at 09:01 PM by 69.169.188.228 -
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Download APM Python Libraries

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Download APM Python Library and Example Problems

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The zipped archives contain a single script file apm.py. To use the APM Python library, include the following at the top of a Python script:

from apm import *

Previous versions of the APM Python libraries are available below in the prior versions section. In general, it is best to use the most current version as it supports the most advanced server features. The product roadmap for this and other libraries are detailed in the release notes section.

Prior Versions

Example applications of the APM Python library include nonlinear programming, nonlinear control, and other applications below.

to:

The product roadmap for this and other libraries are detailed in the release notes section. The zipped archive contains the APM Python library apm.py and a number of example problems in separate folders. Descriptions of the example problems are provided below.

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Nonlinear Programming with Python

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Folder example_hs071: Nonlinear Programming with Python

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Nonlinear Control with Python

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Folder example_nlc: Nonlinear Control with Python

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Nonlinear Estimation and Control with Python

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Folder example_tank_mhe: Nonlinear Estimation and Control with Python

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The the unknown parameters c1 and c2 need to be determined. The parameter c1 is the flow into the tank when the valve is fully open. The parameter c2 is the relationship between the volume of water in the tank and the outlet flow. Notice that this model is nonlinear because the outlet flow depends on the square root of the liquid volume. Nonlinear estimation is a technique to determine parameters based on the measurements. The following Python script uses the process data and the nonlinear model to determine the optimal parameters c1 and c2.

to:

The the unknown parameters c1 and c2 need to be determined. The parameter c1 is the flow into the tank when the valve is fully open. The parameter c2 is the relationship between the volume of water in the tank and the outlet flow. This model is nonlinear because the outlet flow depends on the square root of the liquid volume. Nonlinear estimation is a technique to determine parameters based on the measurements. The script in example_tank_mhe uses the process data and the nonlinear model to determine the optimal parameters c1 and c2.

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After an accurate model of the process is obtained, the model can be used in a Nonlinear Control (NLC) application. A PID controller is compared to the NLC response in the following script.

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After an accurate model of the process is obtained, the model can be used in a Nonlinear Control (NLC) application. A PID controller is compared to the NLC response in the folder example_tank_nlc.

December 14, 2011, at 10:25 PM by 69.169.188.228 -
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The latest APM Python libraries are attached below. Functionality has been tested with Python 2.7 and requires only the base Python installation.

to:

The latest APM Python libraries are attached below. Functionality has been tested with Python 2.7 and requires only the base Python installation. Example applications that use the apm.py library are listed further down on this page.

December 05, 2011, at 11:00 PM by 69.169.188.228 -
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Example applications of the APM Python library include nonlinear programming, nonlinear control, and other applications below.

December 05, 2011, at 10:59 PM by 69.169.188.228 -
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Previous versions of the APM Python libraries are available below. In general, it is best to use the most current version as it supports the most advanced server features. The product roadmap for this and other libraries are detailed in the release notes section.

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Previous versions of the APM Python libraries are available below in the prior versions section. In general, it is best to use the most current version as it supports the most advanced server features. The product roadmap for this and other libraries are detailed in the release notes section.

Prior Versions

December 05, 2011, at 10:57 PM by 69.169.188.228 -
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from apm import *'

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from apm import *

December 05, 2011, at 10:41 PM by 69.169.188.228 -
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Previous versions of the APM Python libraries are available below. In general, it is best to use the most current version as it supports the most advanced server features.

to:

Previous versions of the APM Python libraries are available below. In general, it is best to use the most current version as it supports the most advanced server features. The product roadmap for this and other libraries are detailed in the release notes section.

December 05, 2011, at 10:38 PM by 69.169.188.228 -
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The latest APM Python libraries are attached below.

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The latest APM Python libraries are attached below. Functionality has been tested with Python 2.7 and requires only the base Python installation.

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Previous versions of the APM Python libraries are available below. In general, it is best to use the most current version as it supports the most advanced server features.

December 05, 2011, at 10:35 PM by 69.169.188.228 -
December 05, 2011, at 10:33 PM by 69.169.188.228 -
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Download APM Python Libraries

The latest APM Python libraries are attached below.

The zipped archives contain a single script file apm.py. To use the APM Python library, include the following at the top of a Python script:

from apm import *'

December 05, 2011, at 10:29 PM by 69.169.188.228 -
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November 08, 2011, at 03:58 AM by 69.169.188.228 -
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Python offers a powerful scripting capabilities for solving nonlinear optimization problems. The optimization problem is sent to the APMonitor server and results are returned to the Python script. A web-interface to the solution helps to visualize the dynamic optimization problems. Example applications of nonlinear models with differential and algebraic equations are available for download below.

to:
Python offers a powerful scripting capabilities for solving nonlinear optimization problems. The optimization problem is sent to the APMonitor server and results are returned to the Python script. A web-interface to the solution helps to visualize the dynamic optimization problems. Example applications of nonlinear models with differential and algebraic equations are available for download below.
September 26, 2011, at 04:13 PM by 69.169.188.228 -
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September 16, 2011, at 03:01 PM by 128.187.0.181 -
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Other Applications with Python


September 15, 2011, at 02:16 PM by 128.187.0.181 -
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Nonlinear Estimation and Control with Python

In this case study, a gravity drained tank was operated to generate data. A dynamic model of the process was derived from a material balance. This material balance is displayed below, along with a diagram of the system.

The the unknown parameters c1 and c2 need to be determined. The parameter c1 is the flow into the tank when the valve is fully open. The parameter c2 is the relationship between the volume of water in the tank and the outlet flow. Notice that this model is nonlinear because the outlet flow depends on the square root of the liquid volume. Nonlinear estimation is a technique to determine parameters based on the measurements. The following Python script uses the process data and the nonlinear model to determine the optimal parameters c1 and c2.

After an accurate model of the process is obtained, the model can be used in a Nonlinear Control (NLC) application. A PID controller is compared to the NLC response in the following script.

July 10, 2011, at 03:02 PM by 89.144.73.196 -
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Example #1: Hock-Schittkowsky Test Suite #71 with the IPOPT Solver

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Nonlinear Programming with Python

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Hock-Schittkowsky Test Suite #71

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Example #2: Nonlinear Control with Python

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Nonlinear Control with Python

July 10, 2011, at 02:33 PM by 89.144.73.196 -
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(:title Python Interface to APMonitor:)

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(:title Nonlinear Optimization with Python:)

July 10, 2011, at 02:24 PM by 89.144.73.196 -
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Example #2: Nonlinear Control with Python with the APOPT solver

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Example #2: Nonlinear Control with Python

July 10, 2011, at 02:20 PM by 89.144.73.196 -
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(:title Python Interface to APMonitor:) (:keywords nonlinear, Python, model, predictive control, APMonitor, differential, algebraic, modeling language:) (:description Use APMonitor with the power of Python scripting language:)

Python for APMonitor

Python offers a powerful scripting capabilities for solving nonlinear optimization problems. The optimization problem is sent to the APMonitor server and results are returned to the Python script. A web-interface to the solution helps to visualize the dynamic optimization problems. Example applications of nonlinear models with differential and algebraic equations are available for download below.


Example #1: Hock-Schittkowsky Test Suite #71 with the IPOPT Solver


Example #2: Nonlinear Control with Python with the APOPT solver