William E. Hart's Blog

Ignacio Grossmann and Jon Lee recently announced the CMU-IBM Cyberinfrastructure Collaborative site for MINLP .  The goal of this web site is to create a library of optimization problems in different application areas in which one or several alternative models are presented with their derivation. In addition, each model has one or several instances that can serve to test various algorithms. This effort is different from other test problem collections by requiring a description of the problem, and encouraging the contribution of alternate modeling formulations.  Thus, the actual models in this collection may be MILP or NLP formulations that simplify a nonlinear problem, including simplifications of other MINLP formulations. As it happens, Cindy Phillips, Regan Murray and I are working on a paper that describes our work on sensor placement for water security, where we describe various MILP formulations for this nonlinear application.  I guess we should try to add our models to this repos

Python's setuptools package includes the easy_install script, which provides a convenient mechanism for installing a Python package from the PyPi repository.  Normally, easy_install installs a Python package in the Python site packages directory.  However, I recently discovered that easy_install can download the source for Python package.  For example, the following command downloads the Coopr optimization package into the coopr directory: easy_install -q --editable --build-directory . Coopr I had to browse a variety of web pages before I figured this syntax out.  Enjoy!

I have been working with software-related intellectual property issues for several years now.  I finally broke down and bought a book to help me get the big picture.  The following book has been remarkably helpful: Intellectual Property and Open Source: A practical guide to protecting code Van Lindberg O'Reilly, 2008 I have been quite surprised how well Lindberg describes the complex legal issues related to intellectual property law.  Lindberg is a lawyer and software developer, and he uses computer science analogies that are quite straightforward.

Here's a fun fact that I wanted to archive...  In a recent COIN-OR email exchange on coin-discuss, John Forrest suggested the following command-line for applying the CBC preprocessor to an MPS file:      cbc xxxxx.mps -preprocess save -heuristic off -maxnode -1 -solve This command will save the cbc-presolved model in the file presolved.mps. The -heuristic off and -maxnode -1 options make cbc stop as quickly as possible.

At PyCon 2009, Aaron Maxwell gave a presentation about the use of BuildBot to support an automated software QA infrastructure. Listening to his talk ( online ) made me think more carefully about the reasons I am not using BuildBot, which I took a look at several years ago.  After working with a custom automated build tool for a few years, I have recently begun using Hudson to automate software quality processes for a variety of open source software packages.  Hudson automates the following QA activities for these packages: portability tests - building packages with different compilers, language versions and compute platforms continuous integration - rapid builds and software tests to provide developers continuous feedback integration tests - builds that test the integration of different software tools archiving QA statistics - test histories, code coverage statistics, build times, etc. managing third-party builds - building third-party libraries that my codes depend on Although I am r

The gcovr command provides a utility for running the gcov command and summarizing code coverage results. This command is inspired by the Python coverage.py package, which provides a similar utility in Python. Further, gcovr can be viewed as a command-line alternative of the lcov utility, which runs gcov and generates an HTML output. The gcovr command currently generates two different types of output: Text Summary For each file that generates gcov statistics, gcovr will summarize the number of lines covered, the percentage of coverage and enumerate the lines that are not covered. Cobertura XML An XML summary of the coverage statistics can be generated in a format that is consistent with Cobertura. I find the text summary quite convenient for interactive assessment of coverage, especially as I design tests to improve coverage. The Cobertura summary can be used by continues build tools like Hudson . For example, see the acro-utilib coverage report that was generated with gcovr,

I just discovered that the talks at PyCon 2009 were video taped! Excellent! I had fun browsing them last night, looking for clues to the challenges that I face managing several complex Python packages. I am not sure how the PyCon organizers justified the cost for doing this, but I think that this was an excellent idea. I would love to see other conferences adopt this idea (or at least support online publishing of electronic slides). I suspect that this would discourage some people from attending a conference. However, people like me are already traveling too much. Thus, the PyCon organizers did not lose anything with me; I was already unable to attend. Further, having access to the presentations makes me more likely to adopt the techniques/approaches that they are presenting! This sounds like a win-win to me!!

After blogging about Python plugin frameworks earlier this year, I wound up implemented a new framework in the PyUtilib software package.  The PyUtilib wiki provides a detailed description of the PyUtilib Plugin Framework , but here's a brief summary: This framework is derived from the Trac plugin framework (a.k.a. component architecture) Provides support for both singleton and non-singleton plugin instances Includes utilities for managing plugins within namespaces The core framework is defined in a single Python module, which can be used independently from PyUtilib PyUtilib also includes commonly use plugins, such as A config-file reader/writer based on ConfigParser Loading utilities for eggs and modules A file manager for temporary files Although I initially resisted the urge to develop my own framework, I was led to develop this because (1) I wanted a light-weight framework like that provided by Trac, but (2) Trac's framework is not particularlly modular within the Trac so

Here's a nice discussion and comparison of Python plugin frameworks that I ran across today: Design Docs Plugins - PiTiViWiKi . This notes that a big difference between Zope and Trac plugins is that Zope defines interfaces which allows for checking interface implementation/definition, as well as facilities for plugin adapters. In this respect, the Envisage Core plugins are similar to Zope.

Updated to include pointers to the PyUtilib Component Architecture and PnP. Various Python projects I am working on could benefit from the use of a Plug-in framework.  However, there does not appear to be a standard Python plug-in framework, though there are some mature packages that support plug-ins. Here's a summary of my recent web research: yapsy - This is a simple plug-in framework that was designed specifically to support plug-ins with no external dependencies. Mary Alchin describes a simple plugin framework , with a similar goal.  His classes provide an API for the plugins, with few supporting features (e.g. searching for plugins). André Roberge has a series of posts that describe the application of plugins to refactor a simple calculator application.  The goal of this is to illustrate the requirements for plugin frameworks, with the goal of identifying best practices for plugins. There are some interesting replies to this post, which consider implementations of the

It does not take much to add a lambda function to a Python class. For example, consider the following: >>> class A: pass >>> f = lambda self,x:x >>> setattr(A,"f",f) This code adds the method f to class A . For example: >>> a=A() >>> a.f(1) 1 However, the f method created this way does not have the expected Python name: >>> A.f.__name__ ' ' Further, defining this value is not possible; the instancemethod f does not have a __name__ attribute. The trick is to name the lambda function before defining the class method: >>> class A: pass >>> f = lambda self,x:x >>> f.__name__ = "f" >>> setattr(A,"f",f) >>> f.__name__ 'f' This is simple, but it took too long to figure this out...

Consider the following use of the standard Unix time command: /usr/bin/time ls -R / If the SIGTERM signal is sent to the time process, then the ls process will continue! This is an unexpected behavior, which is not well-documented. Normally, this is not much of an issue; the process that is monitored will simply terminate quietly. However, when the time utility is used in interactive applications, process interrupts can lead to many unexpected rogue processes. The timer command is a modification of the UNIX timing utility that behaves as expected. When using timer , the SIGTERM signal is sent to the process, which terminates it as expected. The timer command is available in the UTILIB software library, but it can be compiled independently.

There are many tools available that can be used to monitor memory usage in computer programs. However, there are few tools that can be applied to monitor the memory usage of a specific process in an automated manner. Most memory monitoring tools provide a gui that a user can monitor. However, these tools are not useful in contexts where memory must be monitored repeatedly. For example, automated software tests may require checks to validate that the memory usage does not exceed expected limits. The memmon command is a new memory monitoring tool that is included in the UTILIB software library. memmon provides a convenient mechanism to report the maximum amount of memory that a process uses. The memmon command requires the absolute path to the command that will be executed. Beyond that, its default syntax is quite simple: $ ./memmon /bin/sleep 1 53768 Kb used The memmon command can also be used to terminate a process whose memory exceeds a specified threshold: $ ./memmon -k 10 /bin/

I have not blogged much this fall because I have been busy managing a variety of software releases.  I plan to include further details in upcoming blogs, but I thought I would summarize these releases here: acro 2.0 - Acro is A Common Repository for Optimizers that integrates a rich variety of optimization libraries and solvers that have been developed for large-scale engineering and scientific applications. Acro was developed to facilitate the design, development, integration and support of optimization software libraries. Thus, Acro includes both individual optimization solvers as well as optimization frameworks that provide abstract interfaces for flexible interoperability of solver components. Furthermore, many solvers included in Acro can exploit parallel computing resources to solve optimization problems more quickly. utilib 4.0 - Utilib is a library of general-purpose C++ utilities, similar in spirit to the Boost libraries. While generally treated as an Acro package, Utilib i