The Algorithmic Challenge of Efficient Vocabulary Expansion
Learning a new language efficiently requires expanding vocabulary quickly. This article explores the problem of selecting books to maximize vocabulary learning efficiency. While selecting a single book is relatively straightforward, choosing multiple books to cover more vocabulary becomes an NP-hard problem, meaning the computation time for exact solutions grows exponentially with the number of books. Fortunately, this problem falls under submodular problems, allowing the use of approximation algorithms to find near-optimal solutions within a certain accuracy. The article introduces greedy algorithms and their improvements, and recommends the efficient Python library submodlib.
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