Survey calibration has proved useful in increasing the accuracy of estimates in practical applications, but the associated computational costs can lead to difficulties with large data sets. In this project, I derive and implement new numerical optimization routines to weight samples using an Entropy Balance calibration approach. This set of algorithms and their associated Python package include extensions of the problem to sparse data, collinear observables, and infeasible moments. Computational results show significant speedup over the widely-used “ebal” R package.