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Optuna Constrained Tree-Structured Parzen Estimator Is a Joint Density Generalization of c-TPE

arXiv 2606.09889 ↗First posted 3 Jun 2026cs.LGcited by 0

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Abstract (arXiv)

Constrained hyperparameter optimization (HPO) is common in practice, yet Optuna's widely used constrained TPE lacks algorithmic analysis. While c-TPE proposes an expected constrained improvement (ECI) approach assuming independence between the objective and constraints, Optuna uses a single joint density over both. We show that Optuna's constrained TPE is joint c-TPE -- the same ECI acquisition function using a joint likelihood. We demonstrate joint c-TPE is invariant to constraint duplication whereas independent c-TPE degrades as the product accumulates duplicated factors. We outline practical tradeoffs between the formulations and directions for future study.

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References · 5

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  2. 2023Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performancecited by 147
  3. 2014Bayesian Optimization with Unknown Constraintscited by 109
  4. 2025OptunaHub: A Platform for Black-Box Optimizationcited by 14
  5. 2022c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimizationcited by 9

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