Dual Lower Bounds for Approximate Degree and Markov-Bernstein Inequalities

Citation:

M. Bun and Thaler, J., “Dual Lower Bounds for Approximate Degree and Markov-Bernstein Inequalities”, Automata, Languages, and Programming, vol. 7965, pp. 303-314, 2013.
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Abstract:

The ε-approximate degree of a Boolean function f: { − 1, 1} n  → { − 1, 1} is the minimum degree of a real polynomial that approximates f to within ε in the ℓ ∞  norm. We prove several lower bounds on this important complexity measure by explicitly constructing solutions to the dual of an appropriate linear program. Our first result resolves the ε-approximate degree of the two-level AND-OR tree for any constant ε > 0. We show that this quantity is Θ(n‾‾√) , closing a line of incrementally larger lower bounds [3,11,21,30,32]. The same lower bound was recently obtained independently by Sherstov using related techniques [25]. Our second result gives an explicit dual polynomial that witnesses a tight lower bound for the approximate degree of any symmetric Boolean function, addressing a question of Špalek [34]. Our final contribution is to reprove several Markov-type inequalities from approximation theory by constructing explicit dual solutions to natural linear programs. These inequalities underly the proofs of many of the best-known approximate degree lower bounds, and have important uses throughout theoretical computer science.

Notes:

ICALP 2013, winner of Best Paper award for Track A

DOI

Acknowledgements: Supported in part by NSF grant CNS-1237235