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# Improved Lower Bound, and Proof Barrier, for Constant Depth Algebraic Circuits

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LIPIcs.MFCS.2022.18.pdf
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## Acknowledgements

We would like to thank the anonymous reviewers of MFCS 2022 for spotting errors and providing helpful suggestions that improved the presentation.

## Cite As

C. S. Bhargav, Sagnik Dutta, and Nitin Saxena. Improved Lower Bound, and Proof Barrier, for Constant Depth Algebraic Circuits. In 47th International Symposium on Mathematical Foundations of Computer Science (MFCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 241, pp. 18:1-18:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.MFCS.2022.18

## Abstract

We show that any product-depth Δ algebraic circuit for the Iterated Matrix Multiplication Polynomial IMM_{n,d} (when d = O(log n/log log n)) must be of size at least n^Ω(d^{1/(φ²)^Δ}) where φ = 1.618… is the golden ratio. This improves the recent breakthrough result of Limaye, Srinivasan and Tavenas (FOCS'21) who showed a super polynomial lower bound of the form n^Ω(d^{1/4^Δ}) for constant-depth circuits. One crucial idea of the (LST21) result was to use set-multilinear polynomials where each of the sets in the underlying partition of the variables could be of different sizes. By picking the set sizes more carefully (depending on the depth we are working with), we first show that any product-depth Δ set-multilinear circuit for IMM_{n,d} (when d = O(log n)) needs size at least n^Ω(d^{1/φ^Δ}). This improves the n^Ω(d^{1/2^Δ}) lower bound of (LST21). We then use their Hardness Escalation technique to lift this to general circuits. We also show that our lower bound cannot be improved significantly using these same techniques. For the specific two set sizes used in (LST21), they showed that their lower bound cannot be improved. We show that for any d^o(1) set sizes (out of maximum possible d), the scope for improving our lower bound is minuscule: there exists a set-multilinear circuit that has product-depth Δ and size almost matching our lower bound such that the value of the measure used to prove the lower bound is maximum for this circuit. This results in a barrier to further improvement using the same measure.

## Subject Classification

##### ACM Subject Classification
• Theory of computation → Algebraic complexity theory
##### Keywords
• polynomials
• lower bounds
• algebraic circuits
• proof barrier
• fibonacci numbers

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## References

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