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Electronic Colloquium on Computational Complexity

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TR23-018 | 1st March 2023
Qipeng Liu, Ran Raz, Wei Zhan

Memory-Sample Lower Bounds for Learning with Classical-Quantum Hybrid Memory

In a work by Raz (J. ACM and FOCS 16), it was proved that any algorithm for parity learning on $n$ bits requires either $\Omega(n^2)$ bits of classical memory or an exponential number (in~$n$) of random samples. A line of recent works continued that research direction and showed that for ... more >>>


TR23-017 | 21st February 2023
Deepanshu Kush, Shubhangi Saraf

Near-Optimal Set-Multilinear Formula Lower Bounds

The seminal work of Raz (J. ACM 2013) as well as the recent breakthrough results by Limaye, Srinivasan, and Tavenas (FOCS 2021, STOC 2022) have demonstrated a potential avenue for obtaining lower bounds for general algebraic formulas, via strong enough lower bounds for set-multilinear formulas.

In this paper, we make ... more >>>


TR23-016 | 22nd February 2023
Yuval Filmus, Edward Hirsch, Artur Riazanov, Alexander Smal, Marc Vinyals

Proving Unsatisfiability with Hitting Formulas

Revisions: 1

Hitting formulas have been studied in many different contexts at least since [Iwama 1989]. A hitting formula is a set of Boolean clauses such that any two of the clauses cannot be simultaneously falsified. [Peitl and Szeider 2022] conjectured that the family of unsatisfiable hitting formulas should contain the hardest ... more >>>



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