Mathematical Science

This course examines various Operations Research models and their applications in industrial sectors, including transportation problems, aircrew and nurse scheduling, finance and insurance models, assignment problems, and applications in the health industry.

This course covers fundamental matrix theory with emphasis on computational techniques essential in optimization, including solving linear systems and eigenvalue problems.

This course covers research problem formulation, literature review, and preparation of a doctoral research proposal.

This course explores specialized algorithms for efficiently solving large-scale linear programming problems, including parametric programming, bounded-variable algorithms, generalized upper bounding, decomposition, and sparse matrix techniques.

This course includes categorical data analysis, log-linear models, logistic regression, multivariate concepts, multivariate normal distribution, mean vector inference, MANOVA, covariance structure analysis, principal component analysis, factor analysis, and classification and clustering techniques.

This course trains students in writing scientific articles for reputable international journals and preparing their doctoral dissertation.

A comprehensive examination to assess student readiness to begin dissertation research.

Students present their dissertation research plan in a colloquium format.

This course covers fundamental methods of economic analysis, including: production activities (production functions, cost structures, perfect and imperfect competition), individual preferences and demand behavior, and market-based systems such as price formation, efficiency, and welfare analysis.

This course discusses the mathematical foundations and analytical aspects of nonlinear optimization algorithms in finite and infinite dimensions. It emphasizes the structure and computational effectiveness of methods for solving constrained and unconstrained minimization problems.

This course covers network optimization models as applications of graph theory. The discussion ranges from classical network models to optimization applications in modern social networks.

This advanced course extends linear optimization by exploring more sophisticated algorithms and analyzing their computational complexity. It also discusses the relationship between linear optimization and graph optimization.

This course provides theory and applications of experimental design across various fields, including industry, education, management, psychology, and economics.

This course introduces fundamental investment concepts, emphasizing optimization techniques for optimal portfolio diversification as well as simulation methods in financial decision-making.

This course covers models and methods of multi-objective programming. Depending on student interest, selected applications from managerial decision-making and multidisciplinary design engineering are discussed to illustrate practical relevance.

This course offers theory and applications of probability and statistics in various fields such as industry, education, management, psychology, and economics.

This course covers the theory and applications of regression and analysis of variance (ANOVA) across multiple fields including industry, education, psychology, economics, and management.

This course examines econometric models for finance, particularly time series models such as multivariate volatility models, high-frequency volatility models, and univariate volatility models.

This course studies the formulation and analysis of stochastic optimization models in industrial contexts. Topics include univariate, multivariate, and conditional probability distributions, expectations, stochastic processes, moment-generating functions, convergence concepts, and non-homogeneous Poisson processes.

This course discusses the formulation and analysis of convex optimization models, including linear optimization and least squares methods.

This course provides fundamental investment concepts and optimal portfolio diversification strategies, as well as their relationship with optimization models.

A closed examination by an examination committee to assess dissertation progress.

Students defend their dissertation in an open doctoral promotion examination as determined by the university.