TL;DR
This paper establishes strong lower bounds on the size and depth of multilinear formulas computing iterated matrix multiplication, demonstrating limitations of divide-and-conquer algorithms and depth reduction in algebraic complexity.
Contribution
It provides the first tight exponential lower bounds on multilinear formula size for IMM and shows depth limitations for multilinear formulas, extending classical results to multilinear settings.
Findings
Any multilinear formula for IMM_d at depth Δ must have size exponential in Δd^{1/Δ}
Polynomial-sized multilinear formulas for IMM_d require depth at least logarithmic in d
Depth reduction cannot significantly lower depth without superpolynomial size blow-up
Abstract
In this paper, we study the algebraic formula complexity of multiplying many matrices, denoted , and show that the well-known divide-and-conquer algorithm cannot be significantly improved at any depth, as long as the formulas are multilinear. Formally, for each depth , we show that any product-depth multilinear formula for must have size It also follows from this that any multilinear circuit of product-depth for the same polynomial of the above form must have a size of In particular, any polynomial-sized multilinear formula for must have depth , and any polynomial-sized multilinear circuit for must have depth Both these bounds are tight up to constant…
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Videos
Small-Depth Multilinear Formula Lower Bounds for Iterated Matrix Multiplication with Applications· youtube
