Lower bounds for planar Arithmetic Circuits
By: C. Ramya, Pratik Shastri
Potential Business Impact:
Makes computers solve harder math problems faster.
Arithmetic circuits are a natural well-studied model for computing multivariate polynomials over a field. In this paper, we study planar arithmetic circuits. These are circuits whose underlying graph is planar. In particular, we prove an $\Omega(n\log n)$ lower bound on the size of planar arithmetic circuits computing explicit bilinear forms on $2n$ variables. As a consequence, we get an $\Omega(n\log n)$ lower bound on the size of arithmetic formulas and planar algebraic branching programs computing explicit bilinear forms on $2n$ variables. This is the first such lower bound on the formula complexity of an explicit bilinear form. In the case of read-once planar circuits, we show $\Omega(n^2)$ size lower bounds for computing explicit bilinear forms on $2n$ variables. Furthermore, we prove fine separations between the various planar models of computations mentioned above. In addition to this, we look at multi-output planar circuits and show $\Omega(n^{4/3})$ size lower bound for computing an explicit linear transformation on $n$-variables. For a suitable definition of multi-output formulas, we extend the above result to get an $\Omega(n^2/\log n)$ size lower bound. As a consequence, we demonstrate that there exists an $n$-variate polynomial computable by an $n^{1 + o(1)}$-sized formula such that any multi-output planar circuit (resp., multi-output formula) simultaneously computing all its first-order partial derivatives requires size $\Omega(n^{4/3})$ (resp., $\Omega(n^2/\log n)$). This shows that a statement analogous to that of Baur, Strassen (1983) does not hold in the case of planar circuits and formulas.
Similar Papers
Arithmetic Circuits and Neural Networks for Regular Matroids
Combinatorics
Finds best ways to pick items for matroids.
Negations are powerful even in small depth
Computational Complexity
Makes computers solve some problems much faster.
Computing the Elementary Symmetric Polynomials in Positive Characteristics
Computational Complexity
Proves computers can't solve some math problems.