# Disciplined Quasiconvex Programming

arXiv: https://arxiv.org/abs/1905.00562 · HTML: https://mlsanity.com/paper/1905.00562/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2019.

## Built on by (most cited first, 8 of 8)

- [Mixture of Weak & Strong Experts on Graphs](https://mlsanity.com/paper/2311.05185/) (2023)
- [Certification of Distributional Individual Fairness](https://mlsanity.com/paper/2311.11911/) (2023)
- [Disciplined Biconvex Programming](https://mlsanity.com/paper/2511.01813/) (2025)
- [Robust Stability of Neural Network-controlled Nonlinear Systems with Parametric Variability](https://mlsanity.com/paper/2109.05710/) (2021)
- [Disciplined Geodesically Convex Programming](https://mlsanity.com/paper/2407.05261/) (2024)
- [A Novel Black Box Process Quality Optimization Approach based on Hit Rate](https://mlsanity.com/paper/2305.20003/) (2023)
- [A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecast](https://mlsanity.com/paper/2306.01986/) (2023)
- [Disciplined Bilevel Programming](https://mlsanity.com/paper/2609.00644/) (2026)

## Abstract (arXiv)

We present a composition rule involving quasiconvex functions that generalizes the classical composition rule for convex functions. This rule complements well-known rules for the curvature of quasiconvex functions under increasing functions and pointwise maximums. We refer to the class of optimization problems generated by these rules, along with a base set of quasiconvex and quasiconcave functions, as disciplined quasiconvex programs. Disciplined quasiconvex programming generalizes disciplined convex programming, the class of optimization problems targeted by most modern domain-specific languages for convex optimization. We describe an implementation of disciplined quasiconvex programming that makes it possible to specify and solve quasiconvex programs in CVXPY 1.0.