# GENO -- GENeric Optimization for Classical Machine Learning

arXiv: https://arxiv.org/abs/1905.13587 · HTML: https://mlsanity.com/paper/1905.13587/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 12 of 12)

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- [NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning](https://mlsanity.com/paper/2210.00973/) (2022)
- [Optimization and Optimizers for Adversarial Robustness](https://mlsanity.com/paper/2303.13401/) (2023)
- [NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning](https://mlsanity.com/paper/2111.13984/) (2021)
- [Optimization for Classical Machine Learning Problems on the GPU](https://mlsanity.com/paper/2203.16340/) (2022)
- [Optimization for Robustness Evaluation beyond ℓp Metrics](https://mlsanity.com/paper/2210.00621/) (2022)
- [Exact Reformulation and Optimization for Direct Metric Optimization in Binary Imbalanced Classification](https://mlsanity.com/paper/2507.15240/) (2025)
- [Efficient Line Search Method Based on Regression and Uncertainty Quantification](https://mlsanity.com/paper/2405.10897/) (2024)
- [Convexity Certificates from Hessians](https://mlsanity.com/paper/2210.10430/) (2022)
- [Explainability of Text Processing and Retrieval Methods: A Survey](https://mlsanity.com/paper/2212.07126/) (2022)
- [An Algorithm-Centered Approach To Model Streaming Data](https://mlsanity.com/paper/2412.09118/) (2024)
- [Information Preserving Line Search via Bayesian Optimization](https://mlsanity.com/paper/2507.15485/) (2025)

## Abstract (arXiv)

Although optimization is the longstanding algorithmic backbone of machine learning, new models still require the time-consuming implementation of new solvers. As a result, there are thousands of implementations of optimization algorithms for machine learning problems. A natural question is, if it is always necessary to implement a new solver, or if there is one algorithm that is sufficient for most models. Common belief suggests that such a one-algorithm-fits-all approach cannot work, because this algorithm cannot exploit model specific structure and thus cannot be efficient and robust on a wide variety of problems. Here, we challenge this common belief. We have designed and implemented the optimization framework GENO (GENeric Optimization) that combines a modeling language with a generic solver. GENO generates a solver from the declarative specification of an optimization problem class. The framework is flexible enough to encompass most of the classical machine learning problems. We show on a wide variety of classical but also some recently suggested problems that the automatically generated solvers are (1) as efficient as well-engineered specialized solvers, (2) more efficient by a decent margin than recent state-of-the-art solvers, and (3) orders of magnitude more efficient than classical modeling language plus solver approaches.