Experimental algorithmicsEducational UseTechnical DocumentationAcademic DocumentationGreedEx DownloadAlgorEx Download
Both AlgorEx and are aimed at the analysis and evaluation levels of Bloom’s taxonomy. AlgorEx supports the study of any algorithms, while GreedEx is limited to a set of predefined greedy algorithms. Both systems may assist at the following tasks:
- Analyzing the behavior of alternative algorithms for a given problem. In GreedEx, such algorithms are greedy, based on alternative selection functions. GreedEx supports analysis with the aid of visualization and animation facilities.
- Evaluating the different behavior of alternative algorithms for a given problem. GreedEx is restricted to assessing optimality, while AlgorEx allows comparing alternative algorithms with respect to their optimality (for optimization problems) or run times. It also supports plain execution, without comparison between algorithms. AlgorEx supports evaluation with the aid of tabular and diagrammatic summaries.
In addition, GreedEx may assist at understanding the behaviour of the greedy algorithms supported. It includes textual information about the algorithm (problem statement and Java pseudo-code). Moreover, its comprehensive and flexible animation bar, as well as its flexibility to enter input data and select selection functions, allows visualizing and tracing the greedy algorithm.
Both systems can be used in algorithm courses in several ways. We highlight the following ones:
- Demonstrations at the classroom. The instructor may use these systems to illustrate or experiment with the behavior of alternative algorithms. They also give the opportunity to introduce or reinforce concepts related to the scientific method: hypothesis, experiment planning, refutation, counter-example, experimental evidence, and mathematical proof.
- Self-study. The student may use GreedEx to study the problem statement and a code template of a greedy algorithm that solves it. Most importantly, students may use both systems to experiment with alternative algorithms, either provided by the teacher or developed by themselves.
- Documentation. Both systems allow exporting the results of experimentation in standard file formats, which can later be imported in documents.
Finally, both systems support multiple natural languages (currently English and Spanish) and exportation of tables and figures.