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Edge estimation problem in unweighted graphs using local and sometimes global queries is a fundamental problem in sublinear algorithms. It has been observed by Goldreich and Ron (Random Structures & Algorithms, 2008), that weighted edge estimation for weighted graphs require $Omega(n)$ local queries, where $n$ denotes the number of vertices in the graph. To handle this problem, we introduce a new inner product query on matrices. Inner product query generalizes and unifies all previously used local queries on graphs used for estimating edges. With this new query, we show that weighted edge estimation in graphs with particular kind of weights can be solved using sublinear queries, in terms of the number of vertices. We also show that using this query we can solve the problem of the bilinear form estimation, and the problem of weighted sampling of entries of matrices induced by bilinear forms. This work is the first step towards weighted edge estimation mentioned in Goldreich and Ron (Random Structures & Algorithms, 2008).
We study the query complexity of quantum learning problems in which the oracles form a group $G$ of unitary matrices. In the simplest case, one wishes to identify the oracle, and we find a description of the optimal success probability of a $t$-query
We develop a notion of {em inner rank} as a tool for obtaining lower bounds on the rank of matrix multiplication tensors. We use it to give a short proof that the border rank (and therefore rank) of the tensor associated with $ntimes n$ matrix multip
The tendency of semidefinite programs to compose perfectly under product has been exploited many times in complexity theory: for example, by Lovasz to determine the Shannon capacity of the pentagon; to show a direct sum theorem for non-deterministic
We find a limit formula for a generalization of MacDonalds inner product in finitely many variables, using equivariant localization on the Grassmannian variety, and the main lemma from cite{Car}, which bounds the torus characters of the higher c{C}ec
We present inequalities related to generalized matrix function for positive semidefinite block matrices. We introduce partial generalized matrix functions corresponding to partial traces and then provide an unified extension of the recent inequalitie