mlsanityData to 7 Oct 2026

Disciplined Convex-Concave Programming

arXiv 1604.02639 ↗First posted 10 Apr 2016math.OCcited by 29

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Abstract (arXiv)

In this paper we introduce disciplined convex-concave programming (DCCP), which combines the ideas of disciplined convex programming (DCP) with convex-concave programming (CCP). Convex-concave programming is an organized heuristic for solving nonconvex problems that involve objective and constraint functions that are a sum of a convex and a concave term. DCP is a structured way to define convex optimization problems, based on a family of basic convex and concave functions and a few rules for combining them. Problems expressed using DCP can be automatically converted to standard form and solved by a generic solver; widely used implementations include YALMIP, CVX, CVXPY, and Convex.jl. In this paper we propose a framework that combines the two ideas, and includes two improvements over previously published work on convex-concave programming, specifically the handling of domains of the functions, and the issue of nondifferentiability on the boundary of the domains. We describe a Python implementation called DCCP, which extends CVXPY, and give examples.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Apr 2016.

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Citing papers per year: 2021: 11; 2022: 1; 2023: 4; 2024: 4; 2025: 5; 2026: 4.

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  1. 2021Strategic Classification Made Practicalcited by 58
  2. 2023Re-weighting Based Group Fairness Regularization via Classwise Robust Optimizationcited by 20
  3. 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventionscited by 19
  4. 2021Loss-Aversively Fair Classificationcited by 18
  5. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  6. 2024Decision-Focused Learning with Directional Gradientscited by 16
  7. 2021Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classificationcited by 13
  8. 2021Risk-Averse Stochastic Shortest Path Planningcited by 8
  9. 2021Correlation inference attacks against machine learning modelscited by 7
  10. 2021Risk-Averse Decision Making Under Uncertaintycited by 2
  11. 2024Efficient Fairness-Performance Pareto Front Computationcited by 2
  12. 2025Revisiting Frank-Wolfe for Structured Nonconvex Optimizationcited by 2
17 more
  1. 2025Disciplined Biconvex Programmingcited by 2
  2. 2021Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Coloniescited by 1
  3. 2022Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systemscited by 1
  4. 2023Towards Assumption-free Bias Mitigationcited by 1
  5. 2024Training Single-Layer Morphological Perceptron Using Convex-Concave Programmingcited by 1
  6. 2025Scalable DC Optimization via Adaptive Frank-Wolfe Algorithmscited by 1
  7. 2026Accelerating Regularized Attention Kernel Regression for Spectrum Cartographycited by 1
  8. 2021Towards interpretability of Mixtures of Hidden Markov Modelscited by 0
  9. 2021Accounting for Model Uncertainty in Algorithmic Discriminationcited by 0
  10. 2021Least-Squares Linear Dilation-Erosion Regressor Trained using a Convex-Concave Procedurecited by 0
  11. 2023An Encoder-Decoder Approach for Packing Circlescited by 0
  12. 2024Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimizationcited by 0
  13. 2025Morphological Perceptron with Competitive Layer: Training Using Convex-Concave Procedurecited by 0
  14. 2025RDD: Pareto Analysis of the Rate-Distortion-Distinguishability Trade-offcited by 0
  15. 2026The Spectral Neuroncited by 0
  16. 2026Difference-of-Convex Regularization for Graph Learning by Differentiable Programmingcited by 0
  17. 2026Disciplined Bilevel Programmingcited by 0