An Interior-Point Method for a Class of Saddle-Point Problems

By Bjarni V. Halldórsson, Reha H. Tütüncü. 2003

96 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/a9ceefcaa28c17d934929d4fa275766eb551f8ff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dimitris J. Bertsimas
2. Constantine Caramanis
3. Laurent El Ghaoui
4. Mark Koenig
5. Shushang Zhu
6. David B. Brown
7. Shouyang Wang
8. François Oustry

In Management Science and Operations Research

1. Francisco Javier Nogales
2. Victor DeMiguel
3. Dimitris J. Bertsimas
4. Shushang Zhu
5. Duan Li
6. Shouyang Wang
7. Hamed Rahimian
8. Sanjay Mehrotra

In Numerical Analysis

1. Dimitris J. Bertsimas
2. Shushang Zhu
3. Duan Li
4. Shouyang Wang
5. Hamed Rahimian
6. Sanjay Mehrotra
7. Constantine Caramanis
8. Laurent El Ghaoui

Citations in the corpus

• Worst-Case Value-At-Risk and Robust Portfolio Optimization: A Conic Programming Approach (2003, methodology): “Similarly, the paper by Halldorsson and Tutuncu (2000) discusses a robust formulation of Marikowitz’s problem with componentwise bounds on mean and covariance matrix and presents a polynomial time interior-point algorithm for solving it. Our paper extends these results to the context of VaR, with ”
• Worst-Case Conditional Value-at-Risk with Application to Robust Portfolio Management (2009, background): “…and ellipsoidal uncertainty, together with mixture distribution uncertainty, are the most often used uncertainty structures in robust optimization formulations (see, for example, Costa and Paiva 2002, Goldfarb and Iyengar 2003, Halldórsson and Tütüncü 2003 and El Ghaoui, Oks and Oustry 2003).”
• Robust portfolio selection under downside risk measures (2009, methodology): “Halldórsson and Tütüncü (2003) applied their interior-point method for saddle-point problems to the robust mean-variance portfolio selection under the box uncertainty of the elements in the mean vector and the covariance matrix.”
• Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach (2004, unknown): “Our paper differs from Goldfarb and Iyengar (2003), Halldórsson and Tütüncü (2000), and Tütüncü and Koenig (2004) in serval respects.”
• A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms (2009, background): “One approach is to use higher-frequency data, say daily instead of monthly returns (see Jagannathan and Ma (2003)).”
• Robust Asset Allocation (2004, methodology): “To obtain the robust efficient portfolios with highest and lowest expected returns as well as to solve the problem (7) (or, equivalently, the problem (24)) for each intermediate value we use the saddle-point algorithm (SP Algorithm, for short) developed by Halldórsson and Tütüncü (2003).”
• Theory and Applications of Robust Optimization (2010, methodology): “They are able to show that this is a saddle-point problem, and they use an algorithm of Halld´orsson and T¨ut¨unc¨u [86] to compute robust efficient frontiers for this portfolio problem.”
• Distributionally Robust Optimization: A Review (2019, background): “Also, for sets with componentwise bound on the mean and covariance, they cast the problem as a SDP (see also Halldórsson and Tütüncü [131] for a similar result).”