Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization

Alnur Ali Carnegie Mellon University, J. Zico Kolter Carnegie Mellon University, Steven Diamond, Stephen Boyd
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:289-298, 2015.

Abstract

We introduce disciplined convex stochastic programming (DCSP), a modeling framework that can significantly lower the barrier for modelers to specify and solve convex stochastic optimization problems, by allowing modelers to naturally express a wide variety of convex stochastic programs in a manner that reflects their underlying mathematical representation. DCSP allows modelers to express expectations of arbitrary expressions, partial optimizations, and chance constraints across a wide variety of convex optimization problem families (e.g., linear, quadratic, second order cone, and semidefinite programs). We illustrate DCSP’s expressivity through a number of sample implementations of problems drawn from the operations research, finance, and machine learning literatures.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR13-university15f, title = {Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization}, author = {University, Alnur Ali Carnegie Mellon and University, J. Zico Kolter Carnegie Mellon and Diamond, Steven and Boyd, Stephen}, booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence}, pages = {289--298}, year = {2015}, editor = {Meila, Marina and Heskes, Tom}, volume = {R13}, series = {Proceedings of Machine Learning Research}, month = {12--16 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r13/main/assets/university15f/university15f.pdf}, url = {https://proceedings.mlr.press/r13/university15f.html}, abstract = {We introduce disciplined convex stochastic programming (DCSP), a modeling framework that can significantly lower the barrier for modelers to specify and solve convex stochastic optimization problems, by allowing modelers to naturally express a wide variety of convex stochastic programs in a manner that reflects their underlying mathematical representation. DCSP allows modelers to express expectations of arbitrary expressions, partial optimizations, and chance constraints across a wide variety of convex optimization problem families (e.g., linear, quadratic, second order cone, and semidefinite programs). We illustrate DCSP’s expressivity through a number of sample implementations of problems drawn from the operations research, finance, and machine learning literatures.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization %A Alnur Ali Carnegie Mellon University %A J. Zico Kolter Carnegie Mellon University %A Steven Diamond %A Stephen Boyd %B Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2015 %E Marina Meila %E Tom Heskes %F pmlr-vR13-university15f %I PMLR %P 289--298 %U https://proceedings.mlr.press/r13/university15f.html %V R13 %X We introduce disciplined convex stochastic programming (DCSP), a modeling framework that can significantly lower the barrier for modelers to specify and solve convex stochastic optimization problems, by allowing modelers to naturally express a wide variety of convex stochastic programs in a manner that reflects their underlying mathematical representation. DCSP allows modelers to express expectations of arbitrary expressions, partial optimizations, and chance constraints across a wide variety of convex optimization problem families (e.g., linear, quadratic, second order cone, and semidefinite programs). We illustrate DCSP’s expressivity through a number of sample implementations of problems drawn from the operations research, finance, and machine learning literatures. %Z Reissued by PMLR on 04 October 2026.
APA
University, A.A.C.M., University, J.Z.K.C.M., Diamond, S. & Boyd, S.. (2015). Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R13:289-298 Available from https://proceedings.mlr.press/r13/university15f.html. Reissued by PMLR on 04 October 2026.

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