Hardness Condensation for Decision Tree Measures by Restrictions

2026-01-31

AI Summary

This work investigates whether Boolean functions can preserve their decision tree complexity measures—such as block sensitivity, fractional block sensitivity, certificate complexity, and deterministic query complexity—without loss under variable restrictions. By constructing explicit counterexamples, the authors demonstrate for the first time that none of these measures admit lossless compression: even under optimal restrictions, their values can decrease to at most the two-thirds power of the original. Conversely, they also prove that there always exists a restriction preserving each measure to at least the square root of its original value. These results establish a general lower bound for lossy compression of complexity measures, improving upon recent findings presented at STOC 2024, and provide weaker yet meaningful positive guarantees for randomized and quantum query complexities.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Constraint Satisfaction and Optimization: Satisfiability
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

For any Boolean function $f:\{0,1\}^n \to \{0,1\}$ with a complexity measure having value $k \ll n$, is it possible to restrict the function $f$ to $\Theta(k)$ variables while keeping the complexity preserved at $\Theta(k)$? This question, in the context of query complexity, was recently studied by G{\"{o}}{\"{o}}s, Newman, Riazanov and Sokolov (STOC 2024). They showed, among other results, that query complexity can not be condensed losslessly. They asked if complexity measures like block sensitivity or unambiguous certificate complexity can be condensed losslessly? In this work, we show that decision tree measures like block sensitivity and certificate complexity, cannot be condensed losslessly. That is, there exists a Boolean function $f$ such that any restriction of $f$ to $O(\mathcal{M}(f))$ variables has $\mathcal{M}(\cdot)$-complexity at most $\tilde{O}(\mathcal{M}(f)^{2/3})$, where $\mathcal{M} \in \{\mathsf{bs},\mathsf{fbs},\mathsf{C},\mathsf{D}\}$. This also improves upon a result of G{\"{o}}{\"{o}}s, Newman, Riazanov and Sokolov (STOC 2024). We also complement the negative results on lossless condensation with positive results about lossy condensation. In particular, we show that for every Boolean function $f$ there exists a restriction of $f$ to $O(\mathcal{M}(f))$ variables such that its $\mathcal{M}(\cdot)$-complexity is at least $\Omega(\mathcal{M}(f)^{1/2})$, where $\mathcal{M} \in \{\mathsf{bs},\mathsf{fbs},\mathsf{C},\mathsf{UC}_{min},\mathsf{UC}_1,\mathsf{UC},\mathsf{D},\widetilde{\mathsf{deg}},\lambda\}$. We also show a slightly weaker positive result for randomized and quantum query complexity.

Problem

• Boolean function
• decision tree complexity
• block sensitivity
• certificate complexity
• hardness condensation

Innovation

• hardness condensation
• block sensitivity
• certificate complexity
• decision tree complexity
• Boolean functions