scholarly journals The basic problem of vehicle scheduling can be solved by maximum bipartite matching

2015 ◽  
Author(s):  
Viktor S. Árgilán ◽  
János Balogh ◽  
Attila Tóth
Keyword(s):  
Basic Problem ◽  
Vehicle Scheduling ◽  
Bipartite Matching ◽  
Maximum Bipartite Matching

Test Set Embedding into Low-Power BIST Sequences Using Maximum Bipartite Matching

2012 ◽  
Author(s):  
I. Voyiatzis ◽  
K. Axiotis ◽  
N. Papaspyrou ◽  
H. Antonopoulou ◽  
C. Efstathiou
Keyword(s):  
Low Power ◽  
Test Set ◽  
Bipartite Matching ◽  
Maximum Bipartite Matching

scholarly journals Quantum Speedup Based on Classical Decision Trees

Quantum ◽  
2020 ◽  
Vol 4 ◽  
pp. 241
Author(s):  
Salman Beigi ◽  
Leila Taghavi
Keyword(s):  
Decision Tree ◽  
Main Tool ◽  
Upper Bounds ◽  
Query Complexity ◽  
Bipartite Matching ◽  
Quantum Query Complexity ◽  
Maximum Bipartite Matching ◽  
Query Algorithm ◽  
Binary Functions ◽  
Quantum Query

Lin and Lin \cite{LL16} have recently shown how starting with a classical query algorithm (decision tree) for a function, we may find upper bounds on its quantum query complexity. More precisely, they have shown that given a decision tree for a function f:{0,1}n→[m] whose input can be accessed via queries to its bits, and a guessing algorithm that predicts answers to the queries, there is a quantum query algorithm for f which makes at most O(GT) quantum queries where T is the depth of the decision tree and G is the maximum number of mistakes of the guessing algorithm. In this paper we give a simple proof of and generalize this result for functions f:[ℓ]n→[m] with non-binary input as well as output alphabets. Our main tool for this generalization is non-binary span program which has recently been developed for non-binary functions, and the dual adversary bound. As applications of our main result we present several quantum query upper bounds, some of which are new. In particular, we show that topological sorting of vertices of a directed graph G can be done with O(n3/2) quantum queries in the adjacency matrix model. Also, we show that the quantum query complexity of the maximum bipartite matching is upper bounded by O(n3/4m+n) in the adjacency list model.

scholarly journals Clustering Uncertain Data Objects using Jeffreys-Divergence and Maximum Bipartite Matching based Similarity Measure

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Krishna Kumar Sharma ◽  
Ayan Seal ◽  
Anis Yazidi ◽  
Ali Selamat ◽  
Ondrej Krejcar
Keyword(s):  
Similarity Measure ◽  
Uncertain Data ◽  
Bipartite Matching ◽  
Data Objects ◽  
Maximum Bipartite Matching
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