In this paper, we consider a new problem of portfolio optimization using stochastic information. In a setting where there is some uncertainty, we ask how to best select k potential solutions, with the goal of optimizing the value of the best solution. More formally, given a combinatorial problem Π, a set of value functions 𝒱 over the solutions of Π, and a distribution 𝒟 over 𝒱, our goal is to select k solutions of Π that maximize or minimize the expected value of the best of those solutions. For a simple example, consider the classic knapsack problem: given a universe of elements each with unit weight and a positive value, the task is to select r elements maximizing the total value. Now suppose that each element’s weight comes from a (known) distribution. How should we select k different solutions so that one of them is likely to yield a high value? In this work, we tackle this basic problem, and generalize it to the setting where the underlying set system forms a matroid. On the technical side, it is clear that the candidate solutions we select must be diverse and anti-correlated; however, it is not clear how to do so efficiently. Our main result is a polynomial-time algorithm that constructs a portfolio within a constant factor of the optimal.
@InProceedings{drygala_et_al:LIPIcs.ITCS.2025.46, author = {Drygala, Marina and Lattanzi, Silvio and Maggiori, Andreas and Stouras, Miltiadis and Svensson, Ola and Vassilvitskii, Sergei}, title = {{Data-Driven Solution Portfolios}}, booktitle = {16th Innovations in Theoretical Computer Science Conference (ITCS 2025)}, pages = {46:1--46:15}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-361-4}, ISSN = {1868-8969}, year = {2025}, volume = {325}, editor = {Meka, Raghu}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2025.46}, URN = {urn:nbn:de:0030-drops-226740}, doi = {10.4230/LIPIcs.ITCS.2025.46}, annote = {Keywords: solution portfolios, data-driven algorithm design, matroids} }
Feedback for Dagstuhl Publishing