Add to ORKGA popular apprach for solving complex optimization problems is through relaxation: some constraints are removed in order to have a convex problem approximating the original problem. On the other hand, direct approaches for solving such problems are becoming increasingly powerful. This paper examines two cases drawn from data analysis, in order to compare the two techniques.E
Clustering is one of an interesting data mining topics that can be applied in many fields. Recently,...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
We propose a novel clustering-based model-building evolutionary algorithm to tackle optimization pro...
A popular apprach for solving complex optimization problems is through relaxation: some constraints ...
Deterministic branch-and-bound algorithms for continuous global optimization often visit a large num...
The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization. An ...
Data mining is a modern area of science dealing with the learning from given data in order to make ...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2014.This...
An overview of my research contributions in optimization is provided, passing through mixed-integer ...
This work explores data analytics in the development of optimization methodology for global optimiza...
In this thesis we explore different mathematical techniques for extracting information from data. In...
The problem of cluster analysis is formulated as a problem of non-smooth, non-convex optimization, a...
In this dissertation consideration is given to the optimization of a function of n variables subject...
We examine various methods for data clustering and data classification that are based on the minimiz...
The problem of clustering a set of data is a textbook machine learning problem, but at the same time...
Clustering is one of an interesting data mining topics that can be applied in many fields. Recently,...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
We propose a novel clustering-based model-building evolutionary algorithm to tackle optimization pro...
A popular apprach for solving complex optimization problems is through relaxation: some constraints ...
Deterministic branch-and-bound algorithms for continuous global optimization often visit a large num...
The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization. An ...
Data mining is a modern area of science dealing with the learning from given data in order to make ...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2014.This...
An overview of my research contributions in optimization is provided, passing through mixed-integer ...
This work explores data analytics in the development of optimization methodology for global optimiza...
In this thesis we explore different mathematical techniques for extracting information from data. In...
The problem of cluster analysis is formulated as a problem of non-smooth, non-convex optimization, a...
In this dissertation consideration is given to the optimization of a function of n variables subject...
We examine various methods for data clustering and data classification that are based on the minimiz...
The problem of clustering a set of data is a textbook machine learning problem, but at the same time...
Clustering is one of an interesting data mining topics that can be applied in many fields. Recently,...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
We propose a novel clustering-based model-building evolutionary algorithm to tackle optimization pro...