Solution and Investigation of Vector Problems of Combinatorial Optimization on a Set of Polypermutations

2008 ◽  
Vol 40 (12) ◽  
pp. 27-42 ◽  
Author(s):  
Natalya V. Semenova ◽  
Lyudmila N. Kolechkina ◽  
Alla N. Nagornaya
Keyword(s):  
Combinatorial Optimization

Combinatorial Optimization Method Based on Evaluation of Proximate Optimality Using Correlation Coefficient

2015 ◽  
Vol 135 (4) ◽  
pp. 466-467 ◽  
Author(s):  
Masahide Morita ◽  
Hiroki Ochiai ◽  
Kenichi Tamura ◽  
Junichi Tsuchiya ◽  
Keiichiro Yasuda
Keyword(s):  
Combinatorial Optimization ◽  
Correlation Coefficient ◽  
Optimization Method

A Combinatorial Optimization Approach to Solve the Synchronous Optical Network (SONET) Problem

2014 ◽  
Vol 6 (2) ◽  
pp. 9
Author(s):  
Mamoon Alameen ◽  
Mohammad Abdul-Niby ◽  
Tarek Selmi ◽  
Sadeq Damrah
Keyword(s):  
Combinatorial Optimization ◽  
Optical Network ◽  
Optimization Approach

Combinatorial Optimization of Clustering Decisions: An Application to Refine Psychiatric Diagnoses

2018 ◽  
Author(s):  
Jordan Stevens ◽  
Douglas Steinley ◽  
Cassandra L. Boness ◽  
Timothy J Trull ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Alcohol Use Disorder ◽  
Point Of View ◽  
Psychiatric Diagnoses ◽  
Optimal Solutions ◽  
Specific Function ◽  
Significant Information ◽  
Diagnosis Group ◽  
Clustering Problems ◽  
Unique Application

Using complete enumeration (e.g., generating all possible subsets of item combinations) to evaluate clustering problems has the benefit of locating globally optimal solutions automatically without the concern of sampling variability. The proposed method is meant to combine clustering variables in such a way as to create groups that are maximally different on a theoretically sound derivation variable(s). After the population of all unique sets is permuted, optimization on some predefined, user-specific function can occur. We apply this technique to optimizing the diagnosis of Alcohol Use Disorder. This is a unique application, from a clustering point of view, in that the decision rule for clustering observations into the diagnosis group relies on both the set of items being considered and a predefined threshold on the number of items required to be endorsed for the diagnosis to occur. In optimizing diagnostic rules, criteria set sizes can be reduced without a loss of significant information when compared to current and proposed, alternative, diagnostic schemes.

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