No Arabic abstract
Hr{a}stad 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 Hr{a}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 functions 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 Hr{a}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 Hr{a}stads result.
We demonstrate a lower bound technique for linear decision lists, which are decision lists where the queries are arbitrary linear threshold functions. We use this technique to prove an explicit lower bound by showing that any linear decision list computing the function $MAJ circ XOR$ requires size $2^{0.18 n}$. This completely answers an open question of Tur{a}n and Vatan [FoCM97]. We also show that the spectral classes $PL_1, PL_infty$, and the polynomial threshold function classes $widehat{PT}_1, PT_1$, are incomparable to linear decision lists.
We develop a notion of {em inner rank} as a tool for obtaining lower bounds on the rank of matrix multiplication tensors. We use it to give a short proof that the border rank (and therefore rank) of the tensor associated with $ntimes n$ matrix multiplication over an arbitrary field is at least $2n^2-n+1$. While inner rank does not provide improvements to currently known lower bounds, we argue that this notion merits further study.
We prove a query complexity lower bound for $mathsf{QMA}$ protocols that solve approximate counting: estimating the size of a set given a membership oracle. This gives rise to an oracle $A$ such that $mathsf{SBP}^A otsubset mathsf{QMA}^A$, resolving an open problem of Aaronson [2]. Our proof uses the polynomial method to derive a lower bound for the $mathsf{SBQP}$ query complexity of the $mathsf{AND}$ of two approximate counting instances. We use Laurent polynomials as a tool in our proof, showing that the Laurent polynomial method can be useful even for problems involving ordinary polynomials.
Dawar and Wilsenach (ICALP 2020) introduce the model of symmetric arithmetic circuits and show an exponential separation between the sizes of symmetric circuits for computing the determinant and the permanent. The symmetry restriction is that the circuits which take a matrix input are unchanged by a permutation applied simultaneously to the rows and columns of the matrix. Under such restrictions we have polynomial-size circuits for computing the determinant but no subexponential size circuits for the permanent. Here, we consider a more stringent symmetry requirement, namely that the circuits are unchanged by arbitrary even permutations applied separately to rows and columns, and prove an exponential lower bound even for circuits computing the determinant. The result requires substantial new machinery. We develop a general framework for proving lower bounds for symmetric circuits with restricted symmetries, based on a new support theorem and new two-player restricted bijection games. These are applied to the determinant problem with a novel construction of matrices that are bi-adjacency matrices of graphs based on the CFI construction. Our general framework opens the way to exploring a variety of symmetry restrictions and studying trade-offs between symmetry and other resources used by arithmetic circuits.
In this paper we give lower bounds for the representation of real univariate polynomials as sums of powers of degree 1 polynomials. We present two families of polynomials of degree d such that the number of powers that are required in such a representation must be at least of order d. This is clearly optimal up to a constant factor. Previous lower bounds for this problem were only of order $Omega$($sqrt$ d), and were obtained from arguments based on Wronskian determinants and shifted derivatives. We obtain this improvement thanks to a new lower bound method based on Birkhoff interpolation (also known as lacunary polynomial interpolation).