Game Theory and its Applications (advanced course, for senior and graduates)
     (Course notes are only available to the students enrolled in this course)

                   1. Introduction

                   2. Static games with complete information;

                   3. Dynamic games with complete information;

                   4. Static games with incomplete information (Beyesian games);

                   5. Dynamic games with incomplete information;

                   6. Mechanisim design and principal-agent theory;

                   7. Evolutionary game theory and behavioral game theory;

                   8. Cooperative game theory.

Network Optimization (for senior and graduates)
     (Course notes are only available to the students enrolled in this course)

Contents: 1. Introduction: Graphs and Networks; Combinatorial Optimization; Computational Complexity; P and NP

                  2.  Minimum Spanning Tree (Kruskal Algorithm; Prim Algorithm; Sollin Algorithm); Minimum Arberosence and Maximum Branchings (Edmons Algorithm)

                  3. Introduction to Integer Programming: Cut-Plane, Branch and Bound, Totally Unimodular Matrix

                  4. Introduction to Dynamic Programming

                  5. Shortest Path Problem: Topological Sorting; Label-setting Algorithm; Label-correcting Algorithm; Dijkstra Algorithm; Bellman-Ford; Floyd-Warshall

                  6. Maximum Flow Problem: Ford-Fulkerson; Maximal Capacity Argumenting; Capacity Scaling; Shortest Path Argumenting; Highest-label Preflow-push

                  7. Minimum Cost Flow: Cycle-Canceling; Primal-Dual Algorithm; Out-of-kilter Algorithm; Relaxation Algorithm; Network Simplex Algorithm

                  8. Maximum Matching and Wighted Matching: on Bipartite Graphs; on General Graphs
 

                   1. Introduction: Growth of Functions; Divide-and-conquer; Strassen's Matrix Multiplication

                   2. Recurrence: Solving methods and example; Master Theorem

                   3. Randomized algorithm and Probability analysis

                   4. Sorting algorithms

                   5. Dynamic Programming

                   6. Greedy algorithms

                   7. Graphs: DFS and BFS etc.; Spanning tree; Shortest Path

                   8. Network flow: Maximum Flow; Minimum Cost Flow

                   9. Computational Complexity: NP-Complete Theory

                 10. Approaximate Algorithms: Introduciton and Examples

Experiments in Mathematics (for undergraduates)

     (Course notes are only available to the students enrolled in this course)

Mathematical Modeling (for undergraduates)

     (Course notes are only available to the students enrolled in this course)

Introduction to Production and Operations Management (POM): Deterministic Models and Algorithms

     (Course notes are only available to the students enrolled in this course)

Introduction to Supply Chain Management (SCM): Stochastic Models and Algorithms

     (Course notes are only available to the students enrolled in this course)

Contents: 1. Introduction to Local Search and Fundations of Analysis for Algorithms

                  2. Neural Networks

                  3. Simulated Annealing

                  4. Taboo Search

                  5. Evolutionary Computation (GA, EP, ES)

                  6. Lagrangean Relaxation

Optimization Methods / Linear and Nonlinear Programming (for undergraduates and graduates of any level)

Contents: 1. Introduction to Optimization Problems & Modeling

                  2. Introduction to Convex Analysis

                  3. Simplex method and Its Extensions

                  4. Duality Theory for Linear Programming

                  5. Introduction to Computational Complexity

                  6.Introduction to  Inter-Point Algorithm

                  7. Unconstrained Nonlinear Programming

                  8. Constrained Nonlinear Programming

                  9. Multi-Creteria Programming

                  10. Goal Programming

Numerical Analysis (for undergraduates and graduates of any level)

Contents: 1. Introduction to Numerical Analysis

                  2. Interpolation

                  3. Approaximation

                  4. Numerical Intgration

                  5. Numerical Solutions for ODE

                  6. Numerical Solutions for Nonlinear Equations

                  7. Numerical Solutions for Linear Equations: Direct methods and Iterative methods

                  8. Numerical Solutions for Charateristic Values and Charateristic Vectors of Matrices

Linear Algebra and Analytical Geometry (for freshmen; contents: omitted)

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