We use a technique of “lifting” functions introduced by Krause and Pudlak [Theor. Comput. Sci., 1997], to amplify degree-hardness measures of a function to corresponding monomial-hardness properties of the lifted function. We then show that any symmetric function F projects onto a “lift” of another suitable symmetric function f . These two key results enable us to prove several results on the complexity of symmetric functions in various models, as given below: 1. We provide a characterization of the approximate spectral norm of symmetric functions in terms of the spectrum of the underlying predicate, affirming a conjecture of Ada et al. [APPROX-RANDOM, 2012] which has several consequences. 2. We characterize symmetric functions computable by quasi-polynomial sized Threshold of Parity circuits. 3. We show that the approximate spectral norm of a symmetric function f characterizes the (quantum and classical) bounded error communication complexity of f o XOR. 4. Finally, we characterize the weakly-unbounded error communication complexity of symmetric XOR functions, resolving a weak form of a conjecture by Shi and Zhang [Quantum Information & Computation, 2009]
@InProceedings{chattopadhyay_et_al:LIPIcs.FSTTCS.2017.23, author = {Chattopadhyay, Arkadev and Mande, Nikhil S.}, title = {{A Lifting Theorem with Applications to Symmetric Functions}}, booktitle = {37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2017)}, pages = {23:1--23:14}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-055-2}, ISSN = {1868-8969}, year = {2018}, volume = {93}, editor = {Lokam, Satya and Ramanujam, R.}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2017.23}, URN = {urn:nbn:de:0030-drops-83839}, doi = {10.4230/LIPIcs.FSTTCS.2017.23}, annote = {Keywords: Symmetric functions, lifting, circuit complexity, communication com- plexity} }
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