Static Data Structure Lower Bounds Imply Rigidity

Wednesday, April 17, 2019 - 4:00pm to 5:00pm
Alexander Golovnev
Harvard University
We show that static data structure lower bounds in the group (linear) model imply semiexplicit lower bounds on matrix rigidity. In particular, we prove that an explicit lower bound of t > omega(log^2 n) on the cell-probe complexity of linear data structures in the group model, even against arbitrarily small linear space (s = (1 + eps)n), would already imply a semi-explicit (P^NP) construction of rigid matrices with significantly better parameters than the current state of art (Alon, Panigrahy and Yekhanin, 2009). Our results further assert that polynomial (t ≥ n^delta) data structure lower bounds against near-optimal space, would imply super-linear circuit lower bounds for log-depth linear circuits (a four-decade open question). In the succinct space regime (s = n + o(n)), we show that any improvement on current cell-probe lower bounds in the linear model would also imply new rigidity bounds. Our results rely on a new connection between the “inner” and “outer” dimensions of a matrix (Paturi and Pudlak, 2006), and on a new reduction from worst-case to average-case rigidity, which is of independent interest.
Joint work with Zeev Dvir and Omri Weinstein.