Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Facets of the three-index assignment polytope
Discrete Applied Mathematics
An algorithm for the three-index assignment problem
Operations Research
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Approximation algorithms for multi-dimensional assignment problems with decomposable costs
Discrete Applied Mathematics - Special volume: viewpoints on optimization
Computational Optimization and Applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A class of lagrangian relaxation algorithms for the multidimensional assignment problem
A class of lagrangian relaxation algorithms for the multidimensional assignment problem
Computational Optimization and Applications
Selected topics on assignment problems
Discrete Applied Mathematics
Genetic tracker with adaptive neuro-fuzzy inference system for multiple target tracking
Expert Systems with Applications: An International Journal
Force deployment analysis with generalized grammar
Information Fusion
Some assignment problems arising from multiple target tracking
Mathematical and Computer Modelling: An International Journal
Solving the Multidimensional Assignment Problem by a Cross-Entropy method
Journal of Combinatorial Optimization
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Large classes of data association problems in multiple targettracking applications involving both multiple and single sensorsystems can be formulated as multidimensional assignment problems.These NP-hard problems are large scale and sparse with noisyobjective function values, but must be solved in“real-time”. Lagrangian relaxation methods have proven to beparticularly effective in solving these problems to the noise levelin real-time, especially for dense scenarios and for multiple scansof data from multiple sensors. This work presents a new class ofconstructive Lagrangian relaxation algorithms that circumvent some ofthe deficiencies of previous methods. The results of severalnumerical studies demonstrate the efficiency and effectiveness of thenew algorithm class.