Convex Optimization of Graph Laplacian Eigenvalues

E451069

"Convex Optimization of Graph Laplacian Eigenvalues" is a research work by Stephen P. Boyd that develops convex optimization methods to analyze and design graphs via the spectral properties of their Laplacian matrices.

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Predicate Object
instanceOf research work
scientific paper
aimsTo improve graph connectivity via optimization
optimize eigenvalues of graph Laplacians
provide convex formulations for spectral graph problems
appliesTo network design problems
undirected graphs
weighted graphs
assumes Laplacian matrix is symmetric and positive semidefinite
author Stephen P. Boyd NERFINISHED
basedOn convex analysis
matrix analysis
properties of symmetric matrices
characterizes convex sets defined by Laplacian eigenvalue constraints
trade-offs between graph sparsity and connectivity
contributesTo graph design methodologies
optimization-based network design
spectral optimization of graphs
field applied mathematics
convex optimization
graph theory
spectral graph theory
focusesOn analysis of graphs via spectral properties
design of graphs via spectral properties
graph Laplacian eigenvalues
hasApplicationIn communication networks
power networks
sensor networks
social network analysis
language English
provides design rules for graph weights
examples of convex graph design problems
optimization formulations for eigenvalue bounds
relatedTo algebraic connectivity maximization
control of networked systems
distributed algorithms on graphs
robust network design
spectral clustering
studies Laplacian matrix of a graph
algebraic connectivity of graphs
graph connectivity measures
second smallest Laplacian eigenvalue
spectral properties of graph Laplacians
usesMethod convex optimization
eigenvalue optimization
semidefinite programming

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Stephen P. Boyd notableWork Convex Optimization of Graph Laplacian Eigenvalues