## Shrinkage under Random Projections, and Cubic Formula Lower Bounds for AC0

by Yuval Filmus, Or Meir, and Avishay Tal.

What should one highlight? The main result, which establishes an order of magnitude high lower bound on the size of formula for AC0, or the teqniqures used, which demonstrate yet another advantage of using sophisticated random restrictions (rather than straightforwared ones)? Maybe there is no real need to decide. Just enjoy reading the introduction, albeit the two types of random projections are only defined clearly later (at the beginning of Sections 3 and 4, resp).

#### The original abstract

Hastad showed that any De Morgan formula (composed of AND, OR and NOT gates) shrinks by a factor of $O(p^{2})$ under a random restriction that leaves each variable alive independently with probability $p$ [SICOMP, 1998]. Using this result, he gave an $\widetilde{\Omega}(n^{3})$ formula size lower bound for the Andreev function, which, up to lower order improvements, remains the state-of-the-art lower bound for any explicit function.

In this work, we extend the shrinkage result of H?stad to hold under a far wider family of random restrictions and their generalization — random projections. Based on our shrinkage results, we obtain an $\widetilde{\Omega}(n^{3})$ formula size lower bound for an explicit function computed in $\mathbf{AC}^0$. This improves upon the best known formula size lower bounds for $\mathbf{AC}^0$, that were only quadratic prior to our work. In addition, we prove that the KRW conjecture [Karchmer et al., Computational Complexity 5(3/4), 1995] holds for inner functions for which the unweighted quantum adversary bound is tight. In particular, this holds for inner functions with a tight Khrapchenko bound.

Our random projections are tailor-made to the function's structure so that the function maintains structure even under projection --- using such projections is necessary, as standard random restrictions simplify $\mathbf{AC}^0$ circuits. In contrast, we show that any De Morgan formula shrinks by a quadratic factor under our random projections, allowing us to prove the cubic lower bound.

Our proof techniques build on the proof of H?stad for the simpler case of balanced formulas. This allows for a significantly simpler proof at the cost of slightly worse parameters. As such, when specialized to the case of $p$-random restrictions, our proof can be used as an exposition of H?stad's result.

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