Scenario-Based Robust Optimization of Tree Structures

TL;DR

This paper studies scenario-based robust optimization for tree structures like BSTs and Huffman trees, analyzing their complexity, proposing algorithms with optimal or near-optimal guarantees, and exploring fairness in data structures.

Contribution

It introduces the first study of scenario-based robustness for tree structures, providing complexity results, optimal algorithms, and fairness considerations.

Findings

BST problem is NP-hard across metrics.

Achieves a competitive ratio of ⌈log₂(k+1)⌉, proven optimal.

Provides polynomial algorithms for Pareto-optimal trees under uniform scenarios.

Abstract

We initiate the study of tree structures in the context of scenario-based robust optimization. Specifically, we study Binary Search Trees (BSTs) and Huffman coding, two fundamental techniques for efficiently managing and encoding data based on a known set of frequencies of keys. Given $k$ different scenarios, each defined by a distinct frequency distribution over the keys, our objective is to compute a single tree of best-possible performance, relative to any scenario. We consider, as performance metrics, the competitive ratio, which compares multiplicatively the cost of the solution to the tree of least cost among all scenarios, as well as the regret, which induces a similar, but additive comparison. For BSTs, we show that the problem is NP-hard across both metrics. We also show how to obtain a tree of competitive ratio $\lceil \log_2(k+1) \rceil$, and we prove that this ratio is optimal. For Huffman Trees, we show that the problem is, likewise, NP-hard across both metrics; we also give an algorithm of regret $\lceil \log_2 k \rceil$, which we show is near-optimal, by proving a lower bound of $\lfloor \log_2 k \rfloor$. Last, we give a polynomial-time algorithm for computing Pareto-optimal BSTs with respect to their regret, assuming scenarios defined by uniform distributions over the keys. This setting captures, in particular, the first study of fairness in the context of data structures. We provide an experimental evaluation of all algorithms. To this end, we also provide mixed integer linear program formulation for computing optimal trees.