Publications

Journal Articles

A Planner-Trader Decomposition for Multi-Market Hydro Scheduling

K. Schindler; N. Rujeerapaiboon; D. Kuhn; W. Wiesemann 

Operations Research. 2024. Vol. 72, num. 1, p. 185-202. DOI : 10.1287/opre.2023.2456.

A Pareto Dominance Principle for Data-Driven Optimization

T. Sutter; B. Van Parys; D. Kuhn 

Operations Research. 2024. DOI : 10.1287/opre.2021.0609.

Regret Minimization and Separation in Multi-Bidder Multi-Item Auctions

C. Kocyigit; D. Kuhn; N. Rujeerapaiboon 

INFORMS Journal on Computing. 2024. 

Reliable Frequency Regulation through Vehicle-to-Grid: Encoding Legislation with Robust Constraints

D. Lauinger; F. Vuille; D. Kuhn 

Manufacturing & Service Operations Management. 2024. DOI : 10.1287/msom.2022.0154.

Data-Driven Chance Constrained Programs over Wasserstein Balls

Z. Chen; D. Kuhn; W. Wiesemann 

Operations Research. 2024. Vol. 72, num. 1, p. 410–424. DOI : 10.1287/opre.2022.2330.

Frequency Regulation with Storage: On Losses and Profits

D. Lauinger; F. Vuille; D. Kuhn 

2023-06-05. DOI : 10.48550/arXiv.2306.02987.

Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets

M. Li; T. Sutter; D. Kuhn 

2023. DOI : 10.48550/arXiv.2305.19004.

New Perspectives on Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization

S. Shafieezadeh Abadeh; L. Aolaritei; F. Dörfler; D. Kuhn 

2023. DOI : 10.48550/arXiv.2303.03900.

Stability Verification of Neural Network Controllers using Mixed-Integer Programming

R. Schwan; C. N. Jones; D. Kuhn 

IEEE Transactions on Automatic Control. 2023. Vol. 68, num. 12, p. 7514-7529. DOI : 10.1109/TAC.2023.3283213.

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Z. Chen; D. Kuhn; W. Wiesemann 

Operations Research Letters. 2023. Vol. 51, num. 3, p. 226-233. DOI : 10.1016/j.orl.2023.02.008.

Discrete Optimal Transport with Independent Marginals is #P-Hard

B. Taskesen; S. Shafieezadeh Abadeh; D. Kuhn; K. Natarajan 

SIAM Journal on Optimization. 2023. Vol. 33, num. 2, p. 589-614. DOI : 10.1137/22M1482044.

Efficient Learning of a Linear Dynamical System with Stability Guarantees

W. Jongeneel; T. Sutter; D. Kuhn 

IEEE Transactions on Automatic Control. 2023. Vol. 68, num. 5, p. 2790-2804. DOI : 10.1109/TAC.2022.3213770.

A Unified Theory of Robust and Distributionally Robust Optimization via the Primal-Worst-Equals-Dual-Best Principle

J. Zhen; D. Kuhn; W. Wiesemann 

Operations Research. 2023. DOI : 10.1287/opre.2021.0268.

Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution

B. Taskesen; S. Shafieezadeh Abadeh; D. Kuhn 

Mathematical Programming. 2023. Vol. 199, p. 1033–1106. DOI : 10.1007/s10107-022-01856-x.

Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization

V. A. Nguyen; S. Shafieezadeh Abadeh; D. Kuhn; P. Mohajerin Esfahani 

Mathematics of Operations Research. 2023. Vol. 48, num. 1, p. 1-37. DOI : 10.1287/moor.2021.1176.

Distributionally Robust Optimal Allocation with Costly Verification

H. İ. Bayrak; C. Kocyigit; D. Kuhn; M. Pinar 

2022. 

Small Errors in Random Zeroth-Order Optimization are Imaginary

W. Jongeneel; M-C. Yue; D. Kuhn 

2022. 

Metrizing Fairness

Y. Rychener; B. Taskesen; D. Kuhn 

2022. 

Topological Linear System Identification via Moderate Deviations Theory

W. Jongeneel; T. Sutter; D. Kuhn 

IEEE Control Systems Letters. 2022. Vol. 6, p. 307-312. DOI : 10.1109/LCSYS.2021.3072814.

On Linear Optimization over Wasserstein Balls

M-C. Yue; D. Kuhn; W. Wiesemann 

Mathematical Programming. 2022. Vol. 195, num. 1-2, p. 1107–1122. DOI : 10.1007/s10107-021-01673-8.

Robust Multidimensional Pricing: Separation without Regret

C. Kocyigit Yalcin; N. Rujeerapaiboon; D. Kuhn 

Mathematical Programming. 2022. Vol. 196, p. 841–874. DOI : 10.1007/s10107-021-01615-4.

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

V. A. Nguyen; D. Kuhn; P. Mohajerin Esfahani 

Operations Research. 2022. Vol. 70, num. 1, p. 490-515. DOI : 10.1287/opre.2020.2076.

Scenario Reduction Revisited: Fundamental Limits and Guarantees

N. Rujeerapaiboon; K. Schindler; D. Kuhn; W. Wiesemann 

Mathematical Programming. 2022. Vol. 191, p. 207–242. DOI : 10.1007/s10107-018-1269-1.

Mean-Covariance Robust Risk Measurement

V. A. Nguyen; S. Shafieezadeh Abadeh; D. Filipovic; D. Kuhn 

2021. 

Energy and Reserve Dispatch with Distributionally Robust Joint Chance Constraints

C. Ordoudis; V. A. Nguyen; D. Kuhn; P. Pinson 

Operations Research Letters. 2021. Vol. 49, num. 3, p. 291-299. DOI : 10.1016/j.orl.2021.01.012.

From Data to Decisions: Distributionally Robust Optimization is Optimal

B. P. Van Parys; P. Mohajerin Esfahani; D. Kuhn 

Management Science. 2021. Vol. 67, num. 6, p. 3321-3984. DOI : 10.1287/mnsc.2020.3678.

Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

E. de Klerk; D. Kuhn; K. Postek 

Mathematical Programming. 2020. Vol. 181, p. 265–296. DOI : 10.1007/s10107-019-01429-5.

Distributionally Robust Mechanism Design

C. Kocyigit; G. Iyengar; D. Kuhn; W. Wiesemann 

Management Science. 2020. Vol. 66, num. 1, p. 159–189. DOI : 10.1287/mnsc.2018.3219.

Regularization via Mass Transportation

S. Shafieezadeh Abadeh; D. Kuhn; P. Mohajerin Esfahani 

Journal of Machine Learning Research. 2019. Vol. 20, num. 103, p. 1-68.

Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization

N. Rujeerapaiboon; K. Schindler; D. Kuhn; W. Wiesemann 

SIAM Journal on Optimization. 2019. Vol. 29, num. 2, p. 1211-1239. DOI : 10.1137/17M1150670.

“Dice”-sion Making under Uncertainty: When Can a Random Decision Reduce Risk?

E. Delage; D. Kuhn; W. Wiesemann 

Management Science. 2019. Vol. 65, num. 7, p. 3282–3301. DOI : 10.1287/mnsc.2018.3108.

The Decision Rule Approach to Optimisation under Uncertainty: Methodology and Applications

A. Georghiou; W. Wiesemann; D. Kuhn 

Computational Management Science. 2019. Vol. 16, num. 4, p. 545-576. DOI : 10.1007/s10287-018-0338-5.

From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming

P. Mohajerin Esfahani; T. Sutter; D. Kuhn; J. Lygeros 

SIAM Journal on Optimization. 2018. Vol. 28, num. 3, p. 1968-1998. DOI : 10.1137/17M1133087.

Decision Rule Bounds for Two-Stage Stochastic Bilevel Programs

I. Yanikoglu; D. Kuhn 

SIAM Journal on Optimization. 2018. Vol. 28, num. 1, p. 198-222. DOI : 10.1137/16M1098486.

Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls

G. Hanasusanto; D. Kuhn 

Operations Research. 2018. Vol. 66, num. 3, p. 849-869. DOI : 10.1287/opre.2017.1698.

Chebyshev Inequalities for Products of Random Variables

N. Rujeerapaiboon; D. Kuhn; W. Wiesemann 

Mathematics of Operations Research. 2018. Vol. 43, num. 3, p. 887-918. DOI : 10.1287/moor.2017.0888.

Data-Driven Inverse Optimization with Incomplete Information

P. Mohajerin Esfahani; S. Shafieezadeh Abadeh; G. A. Hanasusanto; D. Kuhn 

Mathematical Programming. 2018. Vol. 167, num. 1, p. 191-234. DOI : 10.1007/s10107-017-1216-6.

Data-Driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations

P. Mohajerin Esfahani; D. Kuhn 

Mathematical Programming. 2018. Vol. 171, num. 1-2, p. 115-166. DOI : 10.1007/s10107-017-1172-1.

Ambiguous Joint Chance Constraints under Mean and Dispersion Information

G. A. Hanasusanto; V. Roitch; D. Kuhn; W. Wiesemann 

Operations Research. 2017. Vol. 65, num. 3, p. 751-767. DOI : 10.1287/opre.2016.1583.

A linear programming approach to the optimization of residential energy systems

D. Lauinger; P. Caliandro; J. Van Herle; D. Kuhn 

Journal of Energy Storage. 2016. Vol. 7, p. 24-37. DOI : 10.1016/j.est.2016.04.009.

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

G. A. Hanasusanto; D. Kuhn; W. Wiesemann 

Operations Research Letters. 2016. Vol. 44, num. 1, p. 6-11. DOI : 10.1016/j.orl.2015.10.006.

A Comment on “Computational Complexity of Stochastic Programming Problems”

G. A. Hanasusanto; D. Kuhn; W. Wiesemann 

Mathematical Programming. 2016. Vol. 159, num. 1, p. 557-569. DOI : 10.1007/s10107-015-0958-2.

Robust Growth-Optimal Portfolios

N. Rujeerapaiboon; D. Kuhn; W. Wiesemann 

Management Science. 2016. Vol. 62, num. 7, p. 2090-2109. DOI : 10.1287/mnsc.2015.2228.

Distributionally Robust Control of Constrained Stochastic Systems

B. P. Van Parys; D. Kuhn; P. J. Goulart; M. Morari 

IEEE Transactions on Automatic Control. 2016. Vol. 61, num. 2, p. 430-442. DOI : 10.1109/TAC.2015.2444134.

Generalized Gauss Inequalities via Semidefinite Programming

B. P. G. Van Parys; P. J. Goulart; D. Kuhn 

Mathematical Programming. 2016. Vol. 156, num. 1, p. 271-302. DOI : 10.1007/s10107-015-0878-1.

Financial Optimization: Optimization Paradigms and Financial Planning under Uncertainty

G. Consigli; P. Brandimarte; D. Kuhn 

OR Spectrum. 2015. Vol. 37, num. 3, p. 553-557. DOI : 10.1007/s00291-015-0406-y.

Interdiction Games on Markovian PERT Networks

E. Gutin; D. Kuhn; W. Wiesemann 

Management Science. 2015. Vol. 61, num. 5, p. 999-1017. DOI : 10.1287/mnsc.2014.1973.

A Distributionally Robust Perspective on Uncertainty Quantification and Chance Constrained Programming

G. A. Hanasusanto; V. Roitch; D. Kuhn; W. Wiesemann 

Mathematical Programming. 2015. Vol. 151, num. 1, p. 35-62. DOI : 10.1007/s10107-015-0896-z.

K-Adaptability in Two-Stage Robust Binary Programming

G. A. Hanasusanto; D. Kuhn; W. Wiesemann 

Operations Research. 2015. Vol. 63, num. 4, p. 877-891. DOI : 10.1287/opre.2015.1392.

Generalized Decision Rule Approximations for Stochastic Programming via Liftings

A. Georghiou; W. Wiesemann; D. Kuhn 

Mathematical Programming. 2015. Vol. 152, num. 1-2, p. 301-338. DOI : 10.1007/s10107-014-0789-6.

Distributionally Robust Multi-Item Newsvendor Problems with Multimodal Demand Distributions

G. A. Hanasusanto; D. Kuhn; S. W. Wallace; S. Zymler 

Mathematical Programming. 2015. Vol. 152, num. 1-2, p. 1-32. DOI : 10.1007/s10107-014-0776-y.

Distributionally Robust Convex Optimization

W. Wiesemann; D. Kuhn; M. Sim 

Operations Research. 2014. Vol. 62, num. 6, p. 1358-1376. DOI : 10.1287/opre.2014.1314.

Distributionally Robust Joint Chance Constraints with Second-Order Moment Information

S. Zymler; D. Kuhn; B. Rustem 

Mathematical Programming. 2013. Vol. 137, num. 1-2, p. 167-198. DOI : 10.1007/s10107-011-0494-7.

Worst-Case Value at Risk of Nonlinear Portfolios

S. Zymler; D. Kuhn; B. Rustem 

Management Science. 2013. Vol. 59, num. 1, p. 172-188. DOI : 10.1287/mnsc.1120.1615.

Robust Markov Decision Processes

W. Wiesemann; D. Kuhn; B. Rustem 

Mathematics of Operations Research. 2013. Vol. 38, num. 1, p. 153-183. DOI : 10.1287/moor.1120.0566.

A Polynomial-Time Solution Scheme for Quadratic Stochastic Programs

P. Rocha; D. Kuhn 

Journal of Optimization Theory and Applications. 2013. Vol. 158, num. 2, p. 576-589. DOI : 10.1007/s10957-012-0264-6.

Guest Editorial: Special Issue on Optimal Decision Making under Uncertainty

R. Hochreiter; D. Kuhn 

Computational Management Science. 2012. Vol. 9, num. 1, p. 1-2. DOI : 10.1007/s10287-011-0136-9.

Robust resource allocations in temporal networks

W. Wiesemann; D. Kuhn; B. Rustem 

Mathematical Programming. 2012. Vol. 135, num. 1-2, p. 437-471. DOI : 10.1007/s10107-011-0478-7.

Multi-resource allocation in stochastic project scheduling

W. Wiesemann; D. Kuhn; B. Rustem 

Annals of Operations Research. 2012. Vol. 193, num. 1, p. 193-220. DOI : 10.1007/s10479-008-0486-z.

A constraint sampling approach for multi-stage robust optimization

P. Vayanos; D. Kuhn; B. Rustem 

Automatica. 2012. Vol. 48, num. 3, p. 459-471. DOI : 10.1016/j.automatica.2011.12.002.

Robust Software Partitioning with Multiple Instantiation

S. A. Spacey; W. Wiesemann; D. Kuhn; W. Luk 

INFORMS Journal on Computing. 2012. Vol. 24, num. 3, p. 500-515. DOI : 10.1287/ijoc.1110.0467.

Multistage stochastic portfolio optimisation in deregulated electricity markets using linear decision rules

P. Rocha; D. Kuhn 

European Journal of Operational Research. 2012. Vol. 216, num. 2, p. 397-408. DOI : 10.1016/j.ejor.2011.08.001.

Polynomial Approximations for Continuous Linear Programs

D. Bampou; D. Kuhn 

SIAM Journal on Optimization. 2012. Vol. 22, num. 2, p. 628-648. DOI : 10.1137/110822992.

An Efficient Method to Estimate the Suboptimality of Affine Controllers

M. J. Hadjiyiannis; P. Goulart; D. Kuhn 

IEEE Transactions on Automatic Control. 2011. Vol. 56, num. 12, p. 2841-2853. DOI : 10.1109/TAC.2011.2139390.

Guest Editorial: Special Issue on Computational Finance

D. Kuhn 

Computational Management Science. 2011. Vol. 8, num. 1-2, p. 1-2. DOI : 10.1007/s10287-009-0112-9.

Robust portfolio optimization with derivative insurance guarantees

S. Zymler; B. Rustem; D. Kuhn 

European Journal of Operational Research. 2011. Vol. 210, num. 2, p. 410-424. DOI : 10.1016/j.ejor.2010.09.027.

Primal and dual linear decision rules in stochastic and robust optimization

D. Kuhn; W. Wiesemann; A. Georghiou 

Mathematical Programming. 2011. Vol. 130, num. 1, p. 177-209. DOI : 10.1007/s10107-009-0331-4.

Maximizing the net present value of a project under uncertainty

W. Wiesemann; D. Kuhn; B. Rustem 

European Journal of Operational Research. 2010. Vol. 202, num. 2, p. 356-367. DOI : 10.1016/j.ejor.2009.05.045.

Analysis of the rebalancing frequency in log-optimal portfolio selection

D. Kuhn; D. G. Luenberger 

Quantitative Finance. 2010. Vol. 10, num. 2, p. 221-234. DOI : 10.1080/14697680802629400.

Dynamic Mean-Variance Portfolio Analysis under Model Risk

D. Kuhn; P. Parpas; B. Rustem; R. Fonseca 

Journal of Computational Finance. 2009. Vol. 12, num. 4, p. 91-115. DOI : 10.21314/JCF.2009.202.

Convergent Bounds for Stochastic Programs with Expected Value Constraints

D. Kuhn 

Journal of Optimization Theory and Applications. 2009. Vol. 141, num. 3, p. 597-618. DOI : 10.1007/s10957-008-9476-1.

An Information-Based Approximation Scheme for Stochastic Optimization Problems in Continuous Time

D. Kuhn 

Mathematics of Operations Research. 2009. Vol. 34, num. 2, p. 428-444. DOI : 10.1287/moor.1080.0369.

Valuation of electricity swing options by multistage stochastic programming

G. Haarbrücker; D. Kuhn 

Automatica. 2009. Vol. 45, num. 4, p. 889-899. DOI : 10.1016/j.automatica.2008.11.022.

Bound-based decision rules in multistage stochastic programming

D. Kuhn; P. Parpas; B. Rustem 

Kybernetika. 2008. Vol. 44, num. 2, p. 134-150.

Aggregation and discretization in multistage stochastic programming

D. Kuhn 

Mathematical Programming. 2008. Vol. 113, num. 1, p. 61-94. DOI : 10.1007/s10107-006-0048-6.

Supercurrents through gated superconductor–normal-metal–superconductor contacts: The Josephson transistor

D. Kuhn; N. Chtchelkatchev; G. Lesovik; G. Blatter 

Physical Review B. 2001. Vol. 63, num. 5, p. 054520. DOI : 10.1103/PhysRevB.63.054520.

Conference Papers

Contextual Stochastic Bilevel Optimization

Y. Hu; J. Wang; Y. Xie; A. Krause; D. Kuhn 

2023. 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, December 10-16, 2023. DOI : 10.48550/arXiv.2310.18535.

Distributionally Robust Linear Quadratic Control

B. Taskesen; D. Iancu; C. Kocyigit; D. Kuhn 

2023. 37th Conference on Neural Information Processing Systems (NeurIPS), New Orleans, December 10-16, 2023. DOI : 10.48550/arXiv.2305.17037.

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Y. Rychener; D. Kuhn; T. Sutter 

2023. 40th International Conference on Machine Learning, Honolulu, Hawaii, USA, July 23-29 2023. DOI : 10.48550/arXiv.2306.04174.

On Topological Equivalence in Linear Quadratic Optimal Control

W. Jongeneel; D. Kuhn 

2021. 2021 European Control Conference (ECC), Rotterdam, Netherlands, June 29 – July 2, 2021. p. 2002-2007. DOI : 10.23919/ECC54610.2021.9654863.

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

T. Sutter; A. Krause; D. Kuhn 

2021. 35th Conference on Neural Information Processing Systems (NeurIPS), Virtual, December 7-10, 2021.

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

B. Taskesen; M-C. Yue; J. Blanchet; D. Kuhn; V. A. Nguyen 

2021. 38th International Conference on Machine Learning (ICML 2021), Virtual, July 18-24, 2021. p. 7168-7179.

Distributionally Robust Optimization with Markovian Data

M. Li; T. Sutter; D. Kuhn 

2021. 38th International Conference on Machine Learning, Virtual, July 18-24, 2021. p. 6493-6503.

A Statistical Test for Probabilistic Fairness

B. Taskesen; J. Blanchet; D. Kuhn; V. A. Nguyen 

2021. ACM Conference on Fairness, Accountability, and Transparency, March 3-10, 2021. DOI : 10.1145/3442188.3445927.

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

V. A. Nguyen; S. Shafieezadeh Abadeh; M-C. Yue; D. Kuhn; W. Wiesemann 

2019. 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 8-14, 2019.

Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization

V. A. Nguyen; S. Shafieezadeh Abadeh; M-C. Yue; D. Kuhn; W. Wiesemann 

2019. Neural Information Processing Systems, Vancouver, Canada, December 8-14, 2019.

Wasserstein Distributionally Robust Kalman Filtering

S. Shafieezadeh Abadeh; V. A. Nguyen; D. Kuhn; P. Mohajerin Esfahani 

2018. Neural Information Processing Systems, Montréal, Canada, December 2-8, 2018.

On Risk Reduction in Kelly Betting Using the Conservative Expected Value

N. Rujeerapaiboon; B. R. Barmish; D. Kuhn 

2018. 57th IEEE Conference on Decision and Control, Miami Beach, Florida, USA, December 17-19, 2018. p. 5801-5806. DOI : 10.1109/CDC.2018.8619186.

Distributionally Robust Logistic Regression

S. Shafieezadeh Abadeh; P. Mohajerin Esfahani; D. Kuhn 

2015. Neural Information Processing Systems, Montréal, Canada, December 7-12, 2015.

Robust Data-Driven Dynamic Programming

G. A. Hanasusanto; D. Kuhn 

2013. Neural Information Processing Systems, Lake Tahoe, USA, December 2013.

Risk-averse shortest path problems

C. Gavriel; G. A. Hanasusanto; D. Kuhn 

2012. 2012 IEEE 51st Annual Conference on Decision and Control (CDC), Maui, HI, USA, December 10-13, 2012. p. 2533-2538. DOI : 10.1109/CDC.2012.6426188.

SQPR: Stream query planning with reuse

E. Kalyvianaki; W. Wiesemann; Q. H. Vu; D. Kuhn; P. Pietzuch 

2011. 2011 IEEE International Conference on Data Engineering (ICDE 2011), Hannover, Germany, 11-16 04 2011. p. 840-851. DOI : 10.1109/ICDE.2011.5767851.

Welfare-Maximizing Correlated Equilibria with an Application to Wireless Communication

F. Kong; D. Kuhn; B. Rustem 

2011. 18th IFAC World Congress, Università Cattolica del Sacro Cuore, Milano, Italy, August 2011. p. 8920-8925. DOI : 10.3182/20110828-6-IT-1002.02982.

Hedging Electricity Swing Options in Incomplete Markets

P. Vayanos; W. Wiesemann; D. Kuhn 

2011. 18th IFAC World Congress, Università Cattolica del Sacro Cuore, Milano, Italy, August 2011. p. 846-853. DOI : 10.3182/20110828-6-IT-1002.03528.

A scenario approach for estimating the suboptimality of linear decision rules in two-stage robust optimization

M. J. Hadjiyiannis; P. J. Goulart; D. Kuhn 

2011. 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011), Orlando, FL, USA, 12-15 December 2011. p. 7386-7391. DOI : 10.1109/CDC.2011.6161342.

Decision rules for information discovery in multi-stage stochastic programming

P. Vayanos; D. Kuhn; B. Rustem 

2011. 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011), Orlando, FL, USA, December 12-15, 2011. p. 7368-7373. DOI : 10.1109/CDC.2011.6161382.

Scenario-free stochastic programming with polynomial decision rules

D. Bampou; D. Kuhn 

2011. 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011), Orlando, FL, USA, December 12-15, 2011. p. 7806-7812. DOI : 10.1109/CDC.2011.6161150.

A cutting-plane method for Mixed-Logical Semidefinite Programs with an application to multi-vehicle robust path planning

F. W. Kong; D. Kuhn; B. Rustem 

2010. 2010 49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, USA, December 15-17, 2010. p. 1360-1365. DOI : 10.1109/CDC.2010.5717988.

Linearly Adjustable International Portfolios

R. Fonseca; D. Kuhn; B. Rustem 

2010. ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics, Rhodes, Greece, September 19–25, 2010. p. 338-341. DOI : 10.1063/1.3498469.

Rapid Design Space visualisation through hardware/software partitioning

S. A. Spacey; W. Luk; P. H. J. Kelly; D. Kuhn 

2009. 2009 5th Southern Conference on Programmable Logic (SPL), São Carlos, Brazil, 1-3 04 2009. p. 159-164. DOI : 10.1109/SPL.2009.4914913.

A Stochastic Programming Approach for QoS-Aware Service Composition

W. Wiesemann; R. Hochreiter; D. Kuhn 

2008. 2008 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid), Lyon, France, 19-22 05 2008. p. 226-233. DOI : 10.1109/CCGRID.2008.40.

Numerical Methods to Increase the Value Added

D. Kuhn 

2005. Energy Talks Ossiach ’05, Ossiach, Austria, April 13-15, 2005.

Energy Business and Finance Policy – Parallels in Methodology and Duties

K. Frauendorfer; J. Güssow; D. Kuhn 

2003. 4. Internationales Energiesymposium, Fuschl, Salzburger Land, Austria, September 25, 2003. p. 124-137.

Umsetzung stochastischer Optimierungsmethoden in der Energiewirtschaft

K. Frauendorfer; J. Güssow; G. Haarbrücker; D. Kuhn; G. Ostermaier 

2002. VDI Tagung “IT-Lösungen für die Energiewirtschaft in liberalisierten Märkten”, Schliersee, DE, May 15, 2002. p. 141-151.

Books

Generalized Bounds for Convex Multistage Stochastic Programs

D. Kuhn 

Berlin: Springer-Verlag, 2005.

Book Chapters

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

D. Kuhn; P. Mohajerin Esfahani; V. A. Nguyen; S. Shafieezadeh Abadeh 

Operations Research & Management Science in the Age of Analytics; 2019. p. 130-166.

Optimal Financial Decision Making Under Uncertainty

G. Consigli; D. Kuhn; P. Brandimarte 

Optimal Financial Decision Making under Uncertainty; Springer International Publishing, 2017. p. 255-290.

The Stochastic Time-Constrained Net Present Value Problem

W. Wiesemann; D. Kuhn 

Handbook on Project Management and Scheduling Vol. 2; Springer International Publishing, 2015. p. 753-780.

Barycentric Bounds in Stochastic Programming: Theory and Application

K. Frauendorfer; D. Kuhn; M. Schürle 

Stochastic Programming: The State of the Art, In Honor of George B. Dantzig; New York, NY: Springer New York, 2011. p. 67-96.

Stochastic Optimization of Investment Planning Problems in the Electric Power Industry

D. Kuhn; P. Parpas; B. Rustem 

Energy Systems Engineering; Weinheim: Wiley-VCH, 2008. p. 215-230.

Threshold Accepting Approach to Improve Bound-based Approximations for Portfolio Optimization

D. Kuhn; P. Parpas; B. Rustem 

Computational Methods in Financial Engineering; Berlin: Springer Verlag, 2008. p. 3-26.

Reports

Stochastische Optimierung im Energiehandel: Entscheidungsunterstützung und Bewertung für das Portfoliomanagement

K. Frauendorfer; J. Güssow; G. Haarbrücker; D. Kuhn 

2005

Swing-Optionen im Elektrizitätsmarkt – Bewertung und optimale Ausübungsstrategien komplexer Stromderivate

K. Frauendorfer; G. Haarbrücker; K. Kiske; D. Kuhn 

2005

A Software Tool for the Valuation and Optimal Exercise of Swing Options

G. Haarbrücker; D. Kuhn 

2003