Skip to main navigation
Skip to search
Skip to main content
the Research Portal - University of Namur Home
English
Français
Home
Profiles
Research units
Projects
Research output
Student theses
Equipment
Datasets
Prizes
Activities
Press/Media
Search by expertise, name or affiliation
Recognizing underlying sparsity in optimization
S. Kim, M. Kojima,
P. Toint
Research output
:
Contribution to journal
›
Article
›
peer-review
42
Downloads (Pure)
Overview
Fingerprint
Projects
(1)
Fingerprint
Dive into the research topics of 'Recognizing underlying sparsity in optimization'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Mathematics
Hessian Matrix
100%
Number
50%
Variables
50%
Vectors
50%
Nonlinear
50%
Zeros
50%
Efficiency
50%
Combinatorial Optimization Problem
50%
Linear Transformation
50%
Greedy Algorithm
50%
Polynomial Optimization
50%
Computer Science
Sparsity
100%
Optimization Problem
33%
Hessian Matrix
33%
Vectors
16%
Heuristics
16%
Linear Transformation
16%
Function Problem
16%
Preprocessors
16%
Combinatorial Optimization Problem
16%
Greedy Algorithm
16%
Semidefinite Programming
16%
Economics, Econometrics and Finance
Efficiency
100%
Metaheuristics
100%
Combinatorial Optimization
100%