# A Novel Black Box Process Quality Optimization Approach based on Hit Rate

arXiv: https://arxiv.org/abs/2305.20003 · HTML: https://mlsanity.com/paper/2305.20003/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 72 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 8 of 8)

- [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://mlsanity.com/paper/1703.00443/) (2017)
- [Mean Absolute Percentage Error for regression models](https://mlsanity.com/paper/1605.02541/) (2016)
- [SDPNAL+: A Majorized Semismooth Newton-CG Augmented Lagrangian Method for Semidefinite Programming with Nonnegative Constraints](https://mlsanity.com/paper/1406.0942/) (2014)
- [The ALAMO approach to machine learning](https://mlsanity.com/paper/1705.10918/) (2017)
- [Stability and Generalization for Markov Chain Stochastic Gradient Methods](https://mlsanity.com/paper/2209.08005/) (2022)
- [SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0)](https://mlsanity.com/paper/1710.10604/) (2017)
- [Disciplined Quasiconvex Programming](https://mlsanity.com/paper/1905.00562/) (2019)
- [Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning](https://mlsanity.com/paper/1804.03797/) (2018)

## Abstract (arXiv)

Hit rate is a key performance metric in predicting process product quality in integrated industrial processes. It represents the percentage of products accepted by downstream processes within a controlled range of quality. However, optimizing hit rate is a non-convex and challenging problem. To address this issue, we propose a data-driven quasi-convex approach that combines factorial hidden Markov models, multitask elastic net, and quasi-convex optimization. Our approach converts the original non-convex problem into a set of convex feasible problems, achieving an optimal hit rate. We verify the convex optimization property and quasi-convex frontier through Monte Carlo simulations and real-world experiments in steel production. Results demonstrate that our approach outperforms classical models, improving hit rates by at least 41.11% and 31.01% on two real datasets. Furthermore, the quasi-convex frontier provides a reference explanation and visualization for the deterioration of solutions obtained by conventional models.