"The Computational Infrastructure for Operations Research (COIN-OR**, or simply COIN) project is an initiative to spur the development of open-source software for the operations research community."

"When modeled in the AMPL modeling language, optimization problems may be examined by a set of tools found in the AMPL Library. Dr. Ampl is a meta solver which, by use of the AMPL Library, dissects such models, obtains statistics on their data, is able to symbolically prove or numerically disprove convexity of the functions involved and provides aid in the decision for an appropriate solver. A problem is associated with a number of appropriate solvers available on the NEOS Server for Optimization by means of relational database."

"The skyline problem is to compute the best tuples from a set of ordered d-tuples. The name is originated from what the solution represented on 2d plane resembles the scene that urban buildings comprise. Skyline is one of the recommendation queries, and it is considering multi criteria. It is very interesting problem as well as very useful query. This problem has been being intensively studied for recent years. Today, I’m going to present the problem definition of skyline. Next time, I’ll describe several algorithms for the skyline problem."

"A synchronizing word w for a given synchronizing DFA is called minimal if no proper prefix or suffix of w is synchronizing. We characterize the class of synchronizing automata having finite language of minimal synchronizing words (such automata are called finitely generated). Using this characterization we prove that any such automaton possesses a synchronizing word of length at most 3n ¿ 5. We also prove that checking whether a given DFA $\mathcal{A}$ is finitely generated is co-NP-hard, and provide an algorithm for this problem which is exponential in the number of states $\mathcal{A}.$"

"Many GECCO papers discuss lessons learned in a particular application, but few papers discuss lessons learned over an ensemble of problem areas. A scan of the tables of contents of the Proceedings from GECCO 2005 and 2006 showed no paper title stressing lessons learned although the term "pitfall" appeared occasionally in abstracts, typically applying to a particular practice. We present in this paper a set of broadly applicable "lessons learned" in the application of evolutionary computing (EC) techniques to a variety of problem areas and present advice related to encoding, running, monitoring, and managing an evolutionary computing task."