Suboptimal Solutions to Team Optimization Problems with Stochastic Information Structure

2012 ◽  
Vol 22 (1) ◽  
pp. 212-243 ◽  
Author(s):  
Giorgio Gnecco ◽  
Marcello Sanguineti ◽  
Mauro Gaggero
Keyword(s):  
Information Structure ◽  
Optimization Problems ◽  
Suboptimal Solutions ◽  
Team Optimization

Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data

2010 ◽  
Vol 22 (3) ◽  
pp. 793-829 ◽  
Author(s):  
Giorgio Gnecco ◽  
Marcello Sanguineti
Keyword(s):  
Statistical Learning ◽  
Hilbert Spaces ◽  
Reproducing Kernel ◽  
Optimization Problems ◽  
Reproducing Kernel Hilbert Spaces ◽  
Linear Combinations ◽  
Suboptimal Solutions ◽  
Learning From Data ◽  
Regularization Techniques ◽  
Kernel Hilbert Spaces

Various regularization techniques are investigated in supervised learning from data. Theoretical features of the associated optimization problems are studied, and sparse suboptimal solutions are searched for. Rates of approximate optimization are estimated for sequences of suboptimal solutions formed by linear combinations of n-tuples of computational units, and statistical learning bounds are derived. As hypothesis sets, reproducing kernel Hilbert spaces and their subsets are considered.

A Political Economy Model of a Research University

2009 ◽  
pp. 132-143
Author(s):  
K. Sonin ◽  
I. Khovanskaya
Keyword(s):  
Organizational Structure ◽  
Information Structure ◽  
Research University ◽  
Hiring Decisions ◽  
Voting Rules ◽  
Economy Model ◽  
Trade Offs ◽  
Political Economic ◽  
Optimal Incentives

Hiring decisions are typically made by committees members of which have different capacity to estimate the quality of candidates. Organizational structure and voting rules in the committees determine the incentives and strategies of applicants; thus, construction of a modern university requires a political structure that provides committee members and applicants with optimal incentives. The existing political-economic model of informative voting typically lacks any degree of variance in the organizational structure, while political-economic models of organization typically assume a parsimonious information structure. In this paper, we propose a simple framework to analyze trade-offs in optimal subdivision of universities into departments and subdepartments, and allocation of political power.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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