The topic of dynamic programming (DP) is particularly challenging for learners newly introduced to algorithm design. The library will serve as a learning aid for studying DP. To this end, we design a widely accessible library that can be used by students to visualize and interact with DP algorithms.
The ultimate goal of this library is not to implement visualization for any finite collection of DP problems. Instead, the library will have the capacity to visualize any DP algorithm, provided that the algorithm is implemented correctly.
Our visualization library works with native Python implementations of DP.
The library has three major features:
- Capability to illustrate step-by-step executions of the DP as it fills out the dynamic programming array. This includes visualization of pre-computations and the backtracking process.
- Interactive self-testing feature in which the user will be quizzed on which cells will be used to compute the result, which cell will the result be stored, and what is the value of the result.
dpvis support Python 3.9 and above. The vast majority of users can install dpvis by running:
pip install dpvisYou can test your installation by running one of our many demos.
python demos/knapsack.pyThen, open http://127.0.0.1:8050/ with your favorite browser (make sure that the prefix is "http" rather than "https").
dpvis is developed and maintained by
- Ramiro Deo-Campo Vuong
- Eric Han
- David H. Lee
- Aditya Prasad
- Tianyu Wang
dpvis is released under the MIT License.
