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Revision #2 to TR13-032 | 22nd March 2014 02:15

Dual Lower Bounds for Approximate Degree and Markov-Bernstein Inequalities

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Revision #2
Authors: Mark Bun, Justin Thaler
Accepted on: 22nd March 2014 02:15
Downloads: 442
Keywords: 


Abstract:

The $\epsilon$-approximate degree of a Boolean function $f: \{-1, 1\}^n \to \{-1, 1\}$ is the minimum degree of a real polynomial that approximates $f$ to within $\epsilon$ in the $\ell_\infty$ 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 $\epsilon$-approximate degree of the two-level AND-OR tree for any constant $\epsilon > 0$. We show that this quantity is $\Theta(\sqrt{n})$, closing a line of incrementally larger lower bounds. The same lower bound was recently obtained independently by Sherstov using related techniques. 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 Spalek. 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.


Revision #1 to TR13-032 | 20th September 2013 21:39

Dual Lower Bounds for Approximate Degree and Markov-Bernstein Inequalities





Revision #1
Authors: Mark Bun, Justin Thaler
Accepted on: 20th September 2013 21:39
Downloads: 679
Keywords: 


Abstract:

The $\epsilon$-approximate degree of a Boolean function $f: \{-1, 1\}^n \to \{-1, 1\}$ is the minimum degree of a real polynomial that approximates $f$ to within $\epsilon$ in the $\ell_\infty$ 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 $\epsilon$-approximate degree of the two-level AND-OR tree for any constant $\epsilon > 0$. We show that this quantity is $\Theta(\sqrt{n})$, closing a line of incrementally larger lower bounds. The same lower bound was recently obtained independently by Sherstov using related techniques. 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 Spalek. 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.



Changes to previous version:

This version includes the details of the construction of an explicit dual witness for the OR function at larger values of the error parameter (Appendix B). It also provides an informal discussion of our dual witness for symmetric functions (Section 4) in terms of complementary slackness.


Paper:

TR13-032 | 26th February 2013 23:22

Dual Lower Bounds for Approximate Degree and Markov-Bernstein Inequalities


Abstract:

The $\epsilon$-approximate degree of a Boolean function $f: \{-1, 1\}^n \to \{-1, 1\}$ is the minimum degree of a real polynomial that approximates $f$ to within $\epsilon$ in the $\ell_\infty$ 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 $\epsilon$-approximate degree of the two-level AND-OR tree for any constant $\epsilon > 0$. We show that this quantity is $\Theta(\sqrt{n})$, closing a line of incrementally larger lower bounds. The same lower bound was recently obtained independently by Sherstov using related techniques. 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 Spalek. 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.



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