Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 78 tok/s
Gemini 2.5 Pro 60 tok/s Pro
GPT-5 Medium 28 tok/s Pro
GPT-5 High 33 tok/s Pro
GPT-4o 101 tok/s Pro
Kimi K2 168 tok/s Pro
GPT OSS 120B 452 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Reconstruction of depth-3, top fan-in two circuits over characteristic zero fields (1512.01256v2)

Published 3 Dec 2015 in cs.DS, cs.CC, and cs.DM

Abstract: Reconstruction of arithmetic circuits has been heavily studied in the past few years and has connections to proving lower bounds and deterministic identity testing. In this paper we present a polynomial time randomized algorithm for reconstructing $\Sigma\Pi\Sigma(2)$ circuits over $\mathbb{F}$ ($char(\mathbb{F})=0$), i.e. depth$-3$ circuits with fan-in $2$ at the top addition gate and having coefficients from a field of characteristic $0$. The algorithm needs only a blackbox query access to the polynomial $f \in \mathbb{F}[x_1,\ldots, x_n]$ of degree $d$, computable by a $\Sigma\Pi\Sigma(2)$ circuit $C$. In addition, we assume that "simple rank" of this polynomial (essential number of variables after removing gcd of the two multiplication gates) is bigger than a constant. Our algorithm runs in time $poly(n, d)$ and returns an equivalent $\Sigma\Pi\Sigma(2)$ circuit(with high probability). The problem of reconstructing $\Sigma\Pi\Sigma(2)$ circuits over finite fields was first proposed by Shpilka in [24]. The generalization to $\Sigma\Pi\Sigma(k)$ circuits, $k = O(1)$ (over finite fields) was addressed by Karnin and Shpilka in [15]. The techniques in these previous involve iterating over all objects of certain kinds over the ambient field and thus running time depends on size of the field $\mathbb{F}$. Their reconstruction algorithm uses lower bounds on the lengths of Linear Locally Decodable Codes with $2$ queries. In our settings, such ideas immediately pose a problem and we need new ideas to handle the case of the characteristic $0$ field $\mathbb{F}$. Our main techniques are based on the use of Quantitative Syslvester Gallai Theorems from the work of Barak et.al. [3] to find a small collection of subspaces to project onto. The heart of our paper lies in subtle applications of Quantitative Sylvester Gallai theorems to prove why projections w.r.t. these subspaces can be glued.

Citations (22)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube