Soft Quantization Using Entropic Regularization

The quantization problem aims to find the best possible approximation of probability measures on Rd using finite and discrete measures. The Wasserstein distance is a typical choice to measure the quality of the approximation. This contribution investigates the properties and robustness of the entropy-regularized quantization problem, which relaxes the standard quantization problem. The proposed approximation technique naturally adopts the softmin function, which is well known for its robustness from both theoretical and practicability standpoints. Moreover, we use the entropy-regularized Wasserstein distance to evaluate the quality of the soft quantization problem's approximation, and we implement a stochastic gradient approach to achieve the optimal solutions. The control parameter in our proposed method allows for the adjustment of the optimization problem's difficulty level, providing significant advantages when dealing with exceptionally challenging problems of interest. As well, this contribution empirically illustrates the performance of the method in various expositions.

Automated summary

This contribution investigates the properties and robustness of the entropy-regularized quantization problem and proposes an approximation technique using the softmin function. The quality of the approximation is evaluated using the entropy-regularized Wasserstein distance. The method's performance is empirically illustrated in various scenarios.