<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2304-0106</journal-id>
<journal-title><![CDATA[Anales de la Academia de Ciencias de Cuba]]></journal-title>
<abbrev-journal-title><![CDATA[Anales de la ACC]]></abbrev-journal-title>
<issn>2304-0106</issn>
<publisher>
<publisher-name><![CDATA[Academia de Ciencias de Cuba]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2304-01062022000300009</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Empleo de modelos de optimización matemática en la solución de problemas computacionales]]></article-title>
<article-title xml:lang="en"><![CDATA[Mathematical optimization models for solving computational problems]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rojas Delgado]]></surname>
<given-names><![CDATA[Jairo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Diez]]></surname>
<given-names><![CDATA[Héctor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morel Pérez]]></surname>
<given-names><![CDATA[Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ferri]]></surname>
<given-names><![CDATA[Francesc J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Trujillo Rasúa]]></surname>
<given-names><![CDATA[Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bello]]></surname>
<given-names><![CDATA[Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de las Ciencias Informáticas Centro de Estudios de Matemática Computacional ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de las Ciencias Informáticas Departamento de Ciencias Básicas Facultad 2]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Central Marta Abreu de Las Villas Centro de Investigaciones de la Informática Facultad de Matemática, Física y Computación]]></institution>
<addr-line><![CDATA[ Villa Clara]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de Valencia Departamento de Informática ]]></institution>
<addr-line><![CDATA[ Valencia]]></addr-line>
<country>España</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Grupo de desarrollo independiente  ]]></institution>
<addr-line><![CDATA[ Montevideo]]></addr-line>
<country>Uruguay</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>12</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2304-01062022000300009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2304-01062022000300009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2304-01062022000300009&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción.  La presente investigación se enmarca en la introducción de nuevos métodos computacionales de optimización matemática para el estudio de problemas de estimación de múltiples indicadores.  Métodos.  Se incluyen un grupo de propuestas de algoritmos de aprendizaje, modelados eficientemente por medio de métodos de optimización ajustados a cada problema y sus aplicaciones en la predicción de múltiples indicadores. Se resuelve eficientemente la introducción de regularizadores apropiados para el entrenamiento simultáneo de varias estructuras de aprendizajes de bajo rango y el aprendizaje de funciones de distancias.  Resultados y Discusión.  Se comprobó experimentalmente, sobre 18 conjuntos de datos disponibles. Los resultados muestran superioridad de la propuesta respecto a los algoritmos del estado del arte MSLR y MMR en tanto los tiempos de ejecución son significativamente menores. Los algoritmos meta-heurísticos basados en métodos de continuación propuestos en el presente trabajo obtienen un menor valor del error de generalización y error de entrenamiento, con diferencia estadísticamente significativa, respecto al algoritmo gradiente descendente estocástico en el entrenamiento de redes neuronales artificiales. Los resultados de introducir el algoritmo DMLMTP demuestran una mejora significativa respecto al algoritmo base KNN-SP propuesto previamente como parte del aprendizaje basado en instancias. Conclusiones, se proponen modernos métodos de optimización matemáticas para la solución de problemas de predicción de múltiples indicadores, posiblemente correlacionados los cuales mejoran los resultados de exactitud del estado del arte en tanto su diseño denota una elevada eficiencia. Se logran aplicaciones concretas en la estimación de la aparición a corto y largo plazo de COVID-19 que validan la investigación.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction.  The current research is framed in the introduction of new computational methods of mathematical optimization for multitarget regression problems.  Methods.  A group of proposals of learning algorithms are included, efficiently modeled by means of optimization methods adjusted to each problem and their applications in the multitarget regression. The introduction of appropriate regularizers is efficiently solved for the simultaneous training of several low range learning structures and distance metric learning.  Results and Discussion.  It was tested experimentally, on 18 sets of available data. The results show the superiority of the proposal with respect to the state-of-the-art algorithms MSLR and MMR in that the execution times are significantly lower. The meta-heuristic algorithms based on continuation methods proposed in the present work obtain a lower value of the generalization error and training error, with statistically significant difference, with respect to the Stochastic Gradient Descent algorithm in the training of Artificial Neural Networks. The results of introducing the DMLMTP algorithm demonstrate a significant improvement over the KNN-SP algorithm previously proposed as part of the instance-based learning. As some conclusions, modern mathematical optimization methods are proposed for the solution of prediction problems of multiple indicators possibly correlated which improve the accuracy results of the state of the art as their design denotes high efficiency. Concrete applications are achieved in the estimation of the short- and long-term forecasting of COVID-19 that validate the research.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[optimización matemática]]></kwd>
<kwd lng="es"><![CDATA[predicción de múltiples indicadores]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje de funciones de distancia]]></kwd>
<kwd lng="es"><![CDATA[regresión lineal multivariada]]></kwd>
<kwd lng="en"><![CDATA[mathematical optimization]]></kwd>
<kwd lng="en"><![CDATA[multitarget regression]]></kwd>
<kwd lng="en"><![CDATA[distance metric kearning]]></kwd>
<kwd lng="en"><![CDATA[multivariate response]]></kwd>
</kwd-group>
</article-meta>
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