TL;DR
This paper demonstrates that deterministic decision tree complexity can be lifted to parity decision tree size complexity using small gadgets satisfying the stifling property, advancing understanding of complexity lifting and proof systems.
Contribution
It introduces the concept of stifling gadgets for lifting to PDT size and shows that constant-size gadgets suffice, improving upon previous randomized and larger gadget approaches.
Findings
Constant-size gadgets enable PDT size lifting.
Systematic reduction of lower bounds from resolution width to size.
First known method to lift deterministic decision tree complexity to PDT size.
Abstract
We show that the deterministic decision tree complexity of a (partial) function or relation lifts to the deterministic parity decision tree (PDT) size complexity of the composed function/relation as long as the gadget satisfies a property that we call stifling. We observe that several simple gadgets of constant size, like Indexing on 3 input bits, Inner Product on 4 input bits, Majority on 3 input bits and random functions, satisfy this property. It can be shown that existing randomized communication lifting theorems ([G\"{o}\"{o}s, Pitassi, Watson. SICOMP'20], [Chattopadhyay et al. SICOMP'21]) imply PDT-size lifting. However there are two shortcomings of this approach: first they lift randomized decision tree complexity of , which could be exponentially smaller than its deterministic counterpart when either is a partial function or even a total search…
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Videos
Lifting to Parity Decision Trees Via Stifling· youtube
