https://zhengzhong-you.github.io/tags/gpu-computing/Recent content in GPU Computing on Zhengzhong Ricky YouHugo -- 0.147.2enTue, 04 Aug 2026 00:00:00 +0000https://zhengzhong-you.github.io/papers/torchdcm/Tue, 04 Aug 2026 00:00:00 +0000https://zhengzhong-you.github.io/papers/torchdcm/<p>Under review at <em>INFORMS Journal on Computing</em>.</p> <p>* Corresponding author.</p> <ul> <li>arXiv: <a href="https://arxiv.org/abs/2608.19231" target="_blank">2608.19231</a></li> <li>Repository: <a href="https://github.com/mbc96325/torchdcm" target="_blank">GitHub</a></li> </ul> <p>TorchDCM is a unified PyTorch-native package for discrete choice modeling. It compiles choice data and model specifications into a likelihood engine for estimation, inference, prediction, and structured reporting on CPU and CUDA devices. The package covers multinomial, nested, mixed, ordered, latent-variable, and panel likelihoods, together with constrained parameters, covariance estimation, willingness-to-pay analysis, elasticities, and extensible likelihood components. Aligned synthetic and real-data experiments compare TorchDCM with seven other estimation packages. TorchDCM completes all 45 synthetic cases, runs fastest in every comparable synthetic case, and meets the prespecified final-log-likelihood tolerance whenever at least two comparable solutions are available.</p>