CSEN 1113 Optimization Algorithms

Course Information

Abstract

  • "Optimization problems lie at the heart of operations research where the objective is to find optimal or near-optimal solutions to complex decision-making problems. Examples of these problems are transporta- tion, scheduling, routing, assignment, network optimization, project planning, supply chain management problems and much more. To solve these problems, it’s required to: • formulate mathematical models that are precise representations of the real-life problems • use mathematical optimization techniques to find optimal or near-optimal (if the problem is quite hard) solutions to the formulated problems • use analytical methods to understand more about the model parameters and how to tune them for application in real life. This course focuses on translating real-life problem statements into mathematical models and using various problem-solving techniques to find solutions for P and NP optimization problems.

Outline

  • 1. Introduction: Mathematical Optimization, Problem Modeling 2. Data Structures: Segment Trees, Self-Balanced, Binary Search Trees 3. Graph Algorithms: Lowest Common Ancestor, Heavy-Light Decomposition, Network Flows 4. Search Algorithms: Linear Programming, Integer Programming, Metaheuristics

Objectives

  • By the end of this course, the students will be able to: • Write mathematical model formulation for real-life problems • Describe the computational complexity of the problems • Apply different optimization techniques to solve problem • Perform post-optimality analysis to tune the parameters of problem models
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