approximation algorithm
Approximation algorithms are used in optimization problems where finding the exact solution is computationally expensive or impractical. These algorithms provide near-optimal solutions within guaranteed error bounds, facilitating the handling of large, complex problems.
- A Partition Cover Approach to Tokenization
- A Unified Approach to Submodular Maximization Under Noise
- Dynamic Algorithm for Explainable $k$-medians Clustering under $\ell_p$ Norm
- Efficient Algorithms for Robust and Partial Semi-Discrete Optimal Transport
- Faithful Group Shapley Value
- Nearly-Linear Time and Massively Parallel Algorithms for $k$-anonymity