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OR/MA/ISE 505: Linear Programming

OR/MA/ISE 505: Linear Programming. A Graduate Level Course Shu-Cherng Fang North Carolina State University – Fall 2019. Outline. Course objective Pre-requisites Course content Grading and exams Classroom rules Textbook and references. Course objective.

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OR/MA/ISE 505: Linear Programming

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  1. OR/MA/ISE 505: Linear Programming A Graduate Level Course Shu-Cherng Fang North Carolina State University – Fall 2019

  2. Outline • Course objective • Pre-requisites • Course content • Grading and exams • Classroom rules • Textbook and references

  3. Course objective • This is a course that provides the fundamental understanding to the theory and algorithms of linear optimization. • It involves mathematical analysis, theorem proving, algorithm design and numerical methods. • It is also a preparatory course for ISE/OR students to take their PhD Qualifying Exam on this subject.

  4. Important to know • This course does NOT tell you how to build linear programming models. • This course does NOT tell you how to use Excel Spreadsheets, SAS OPT, MATLAB, LINGO, CPLEX, or any solver.

  5. Prerequisites 1. Matrix Theory 2. Linear Algebra 3. Introduction to OR Homework #1 tells you something.

  6. Course contents 1. Introduction to LP 2. Geometric Interpretation of LP 3. Simplex Method 4. Duality and Sensitivity Analysis 5. Interior Point Method 6. Related Topics

  7. Grading and standard 1. Homework - 20% 2. Two Midterms - 50% 3. Final - 30% A - 88 and above B - 75 to 87 C - 60 to 74 Fail - under 60

  8. Exams • First Midterm: Up to the Simplex Method, closed book exam. • Second Midterm: Up to Duality and Sensitivity, closed book exam with one 4”x6” index card. • Final: Comprehensive with emphasis on the Interior Point Methods with one 8”x11” sheet.

  9. Classroom rules • Rule 1: No late homework without TA's approval • Rule 2: Convince TA for any grade changes. • Rule 3: No make-up exam without my pre-approval or "doctor notes" from hospital.

  10. Textbook • Shu-Cherng Fang and Sarat Puthenpura, “Linear Optimization and Extensions: Theory and Algorithms.” Prentice Hall 1993.

  11. References 1. G. Dantzig, "Linear Programming and Extensions“ (QA265 G3 1963) 2. V. Chvatal, "Linear Programming" (T57.74 C54 1983) 3. K. Murty, "Linear Programming" (T57.74 M87 1983) 4. D. G. Luenberger, "Linear and Nonlinear Programming" (T57.7 L8 1984) 5. S. I. Gass, "Linear Programming" (T57.74 G3 1985) 6. K. Murty, "Linear and Combinatorial Programming“ (T57.74 M87 1985) 7. M. S. Bazaraa, "Linear Programming and Network Flows" (T57.74 B39 1990)

  12. References 8. A. George, J. W. Liu, "Computer Solutions for Large Sparse Definite Systems" (QA188 G46 1981) 9. G. H. Golub, C. F. Van Loan, “Matrix Computations’’ (QA188 G65 1983) 10. N. Meggido, "Progress in Mathematical Programming” (QA402.5 P785 1989) 11. R. Saigal, "Linear Programming" (T57.74 S23 1995) 12. G. Dantzig, M. Thapa, “Linear Programming” (T57.74.D365 1997) 13. D. Bertsimas, J. Tsitsiklis. “Introduction to Linear Optimization” (1997) 14. R. Vanderbei, “Linear Programming: Foundations and Extensions” (T57.74.V36 2008)

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