A local search for a graph clustering problem

2016 ◽  
Author(s):  
Anna Navrotskaya ◽  
Victor Il’ev
Keyword(s):  
Local Search ◽  
Graph Clustering ◽  
Clustering Problem

APPROXIMATE ALGORITHMS FOR GRAPH CLUSTERING PROBLEM

2019 ◽  
pp. 64-77
Author(s):  
V. P. Il’ev ◽  
◽  
S. D. Il’eva ◽  
A. V. Morshinin ◽  
◽  
...  
Keyword(s):  
Graph Clustering ◽  
Approximate Algorithms ◽  
Clustering Problem

The graph clustering problem has a perfect zero-knowledge interactive proof

1999 ◽  
Vol 69 (4) ◽  
pp. 201-206 ◽  
Author(s):  
Alfredo De Santis ◽  
Giovanni Di Crescenzo ◽  
Oded Goldreich ◽  
Giuseppe Persiano
Keyword(s):  
Graph Clustering ◽  
Interactive Proof ◽  
Zero Knowledge ◽  
Clustering Problem

scholarly journals Market Graph Clustering via QUBO and Digital Annealing

2021 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Seo Woo Hong ◽  
Pierre Miasnikof ◽  
Roy Kwon ◽  
Yuri Lawryshyn
Keyword(s):  
Graph Clustering ◽  
Small Subset ◽  
Index Tracking ◽  
Np Hard ◽  
Tracking Problem ◽  
Financial Industry ◽  
Graph Models ◽  
Clustering Problem ◽  
Market Graph ◽  
Common Task

We present a novel technique for cardinality-constrained index-tracking, a common task in the financial industry. Our approach is based on market graph models. We model our reference indices as market graphs and express the index-tracking problem as a quadratic K-medoids clustering problem. We take advantage of a purpose-built hardware architecture to circumvent the NP-hard nature of the problem and solve our formulation efficiently. The main contributions of this article are bridging three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization modeling, to formulate the index-tracking problem as a binary quadratic K-medoid graph-clustering problem. Our initial results show we accurately replicate the returns of various market indices, using only a small subset of their constituent assets. Moreover, our binary quadratic formulation allows us to take advantage of recent hardware advances to overcome the NP-hard nature of the problem and obtain solutions faster than with traditional architectures and solvers.

Adaptive biased random-key genetic algorithm with local search for the capacitated centered clustering problem

2018 ◽  
Vol 124 ◽  
pp. 331-346 ◽  
Author(s):  
Antônio Augusto Chaves ◽  
José Fernando Gonçalves ◽  
Luiz Antonio Nogueira Lorena
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Clustering Problem
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