This Semester (Fall 2016)

INDR 201 Discrete Mathematical Structures

INDR 501 Optimization Models and Algorithms

PREVIOUSLY TAUGHT
COURSES

Here is a list of courses I have taught so far:

Koc University

INDR 201 Discrete Mathematical
Structures (Fall 2011, Fall
2012, Fall 2013, Fall 2014,Fall 2015)

Fundamentals of logic, mathematical
induction, basic set theory, relations and functions, fundamental
principles of counting, inclusion-exclusion principles, basic graph
theory, trees, algorithms for basic industrial engineering and
operations research problems on graphs and networks.

INDR 430/530 Decision Analysis (Fall 2011, Fall
2012)

INDR 484/584 Logistics Management (Spring 2011, Spring 2012, Spring 2013, Spring 2014,Spring 2015,Spring 2016)

Formulation of integer and combinatorial optimization problems Introduction to logistics systems; logistics network design, location models; warehouse design, tactical decisions, operational decisions; transportation management; planning and managing freight transportation; fleet management, vehicle routing problem.

INDR 450/580 Selected Topics in Industrial Engineering: Approximation Algorithms (Spring 2011)

The course covers combinatorial and mathematical programming techniques to derive approximation algorithms for np-hard optimization problems. Possible topics include greedy algorithms for vertex/set cover, approximation schemes via dynamic programming, rounding LP relaxations of integer programs, and semidefinite relaxations. The course is complemented by the implementation of selected algorithms using a high-level language such as MATLAB.INDR 562 Integer and Combinatorial Optimization (Spring 2012, Spring 2013, Spring 2014,Spring 2015,Spring 2016)

Formulation of integer and combinatorial optimization problems. Optimality conditions and relaxations. Polyhedral theory and integer polyhedra. The theory of valid inequalities, strong formulations. Duality and relaxation of integer programming problems. General and special purpose algorithms including branch and bound, decomposition and cutting plane algorithms.

Bilkent University

IE 202 Introduction to Modeling and Optimization (Spring 2006, Spring 2008, Spring 2009, Fall 2009, Spring 2010)

A general overview of operations research, with selected applications from engineering and management systems, and interdisciplinary areas. The methodology of mathematical modeling and its relation to problems in industrial, commercial, and public systems. Introduction to linear programming: the simplex method, duality, sensitivity analysis, and related topics. Network models and project scheduling.

IE 400 Principles of Engineering Management (Fall 2006, Fall 2007)

This course is designed to introduce the engineering students to economic and management concepts. Topics will include economic concepts such as; cash flow, interest rates, rate of return, demand supply relations, product pricing, taxes, inflation, and related subjects; and management analysis such as management layers, network analysis, project management via CPM/PERT networks, optimization concepts, linear programming, and decision analysis. The course also includes use of related software.

IE 500 Mathematics of Operations Research (Fall 2007, Fall 2008, Fall 2009, Fall 2010)

Introduction to methods of proof, sets and functions, metric spaces, functions on metric spaces, differential and integral equations, fundamentals of linear algebra.

IE 513 Linear Programming (Fall 2005, Fall 2006)

Theory, algorithms, and computational aspects of linear programming. Formulation of problems as linear programs. Development of simplex algorithm, geometry of simplex method, duality theory, and economic interpretations. Sensitivity analysis. Variants of simplex method.

IE 519 Approximation Algorithms (Fall 2008, Fall 2010)

The course covers combinatorial and mathematical programming techniques to derive approximation algorithms for np-hard optimization problems. Possible topics include greedy algorithms for vertex/set cover, approximation schemes via dynamic programming, rounding LP relaxations of integer programs, and semidefinite relaxations. The course is complemented by the implementation of selected algorithms using a high-level language such as MATLAB.

IE 614 Nonlinear Programming (Spring 2007, Spring 2010)

Local and global optima. Newton-type, quasi-Newton, and conjugate gradient methods for unconstrained optimization. Kuhn-Tucker theory and Lagrangean duality. Algorithms for linearly constrained optimization, including steepest ascent and reduced gradient methods with applications to linear and quadratic programming. Nonlinearly constrained optimization including penalty and barrier function methods, reduced and projected gradient methods, Lagrangean methods. Computer implementation.

Stony Brook University

EAS 10

A course intended to integrate first-semester Stony Brook freshmen into the university community and particularly into the College of Engineering and Applied Sciences. Special emphasis is placed on basic computing skills, internet access, and the programs, laboratories, and library of the college.

AMS 30

An introduction to graph theory and combinatorial analysis. The emphasis is on solving applied problems rather than on theorems and proofs. Techniques used in problem solving include generating functions, recurrence relations, and network flows. This course develops the type of mathematical thinking that is fundamental to computer science and operations research.

AMS 54

Theoretical and computational properties of discrete and nonlinear optimization problems: integer programming, including cutting plane and branch and bound algorithms, necessary and sufficient conditions for optimality of nonlinear programs, and performance of selected nonlinear programming algorithms. This course is offered as both MBA 544 and AMS 544.

AMS 64

The course is designed for second- and third-year graduate students with a strong foundation in linear algebra and analysis who wish to pursue research in applied mathematics. Varying topics from nonlinear programming and optimization to applied graph theory and applied combinatorics may be offered concurrently.

Cornell University

OR&IE 52

A course for Master of Engineering students that deals with applications and methodologies of dynamic programming, integer programming, and large-scale linear programming.

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