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      MLSysOps: Machine Learning for Autonomic System Operation in the Heterogeneous Edge-Cloud Continuum

      Scientific Responsible

      Spyros LalisSpyros Lalis, Professor
      E-mail: lalis@e-ce.uth.gr

      TitleMLSysOps: Machine Learning for Autonomic System Operation in the Heterogeneous Edge-Cloud Continuum
      Duration2023 – 2025
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ECE337 Linear Programming

Home » Studies » Undergraduate Studies » Undergraduate Courses » ECE337 Linear Programming
Subject AreaApplications and Foundations of Computer Science
SemesterSemester 5 – Fall
TypeElective
Teaching Hours4
ECTS6
Prerequisites
  • ECE117 Linear Algebra
Course Sitehttps://eclass.uth.gr/courses/E-CE_U_119/
Course Director

Emmanouil VavalisEmmanouil Vavalis, Professor
E-mail: mav@e-ce.uth.gr

Course Instructor
  • Emmanouil Vavalis, Professor
    E-mail: mav@e-ce.uth.gr
  • Description
  • Learning Outcomes

Introduction: Optimization, Problems encountered, Size of Problems, Iterative Algorithms and their Convergence. Basic Properties of Linear Programs: Introduction, Examples of Linear Program Problems, Basic Solutions, The Fundamental Theorem of Linear Programming, Relations to Convexity. Simplex Method: Pivots, Adjacent Extreme Points, Determination of Minimum Feasible Solution.Computational Procedures—Simplex Method: Artificial Variables, Variables with Upper Bounds.Matrix Form of Simplex Method, Revised Simplex Method, Duality: Dual Linear Programs, The Theorem of Duality, Relationto the Simplex Procedure,Sensitivity and Complementarity Slackness, Simplex Dual Method. Primal-Dual Algorithm, Reduction of Linear Inequalities (Redundant Equations, Null Variables, Non-external Variables, Applications).Transportation and Flow Network Problems: The Transportation Problem, Determination of a Basic Feasible Solution, Triangulation of Basis, Simplex Method for Transportation Problems, The Assignment Problem, Basic Network Concepts, Minimum Cost Flowς, Maximum Flow, Primal-Dual Transportation Algorithm, Summary. Karmakar’s Algorithm.

Students will be able to

  • Understand the basic concepts of Linear Programming
  • Model real life practical optimization problems of certain complexity as Linear Programming problems
  • Solve practical Linear Programming as those that are produced from various applications. (E.g., economics,networks)
  • Analyze the accuracy and other characteristics of the obtained solutions

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Contact Info

  • Sekeri – Cheiden Str, Pedion Areos, Volos
  • +30 24210 74967
  • +30 24210 74934
  • Email: gece@e-ce.uth.gr

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