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| Mathematical Programming, Probability, and Mathematical Statistics (School of Science) |
| MATP-4600 Probability Theory and Applications Axioms of probability, joint and conditional probability, random variables, probability density and distribution functions, expectation, functions of random variables, and limit theorems. Applications of probability to models in operations research, including queuing theory and Markov chains. (Cross listed as DSES-4750. Students cannot obtain credit for both this course and DSES-4750.) Prerequisite: MATH-1020 or equivalent or permission of instructor. Fall term annually. 4 credit hours |
| MATP-4620 Mathematical Statistics A course in the theory of statistics that will provide students with a basic foundation for more specialized statistical methodology courses. Topics include sampling and sampling distributions; point estimation including method of moments, maximum likelihood estimation, uniform minimum variance estimation, and properties of the associated estimators; hypothesis testing including uniformly most powerful, likelihood ratio, chi-square goodness-of-fit tests, and tests for independence. The course concludes with an introduction to linear statistical models. (Cross listed as DSES-4760. Students cannot obtain credit for both this course and DSES-4760.) Prerequisite: DSES-4750 or MATP-4600 or equivalent calculus-based course. Spring term annually. 4 credit hours |
| MATP-4700 Mathematical Models of Operations Research Introduction to deterministic models of operations research including linear programming formulations, the simplex algorithm, degeneracy, geometry of convex polyhedra, duality theory, and sensitivity analysis. Special linear programming models for assignment, transportation, and network problems. Integer programming formulations along with branch and bound solution. Dynamic programming. (Cross listed as DSES-4770. Students cannot obtain credit for both this course and DSES-4770.) Prerequisites: MATH-1020, and MATH-2010 or ENGR-1100, or equivalent, or permission of instructor. Fall term annually. 4 credit hours |
| MATP-4820 Computational Optimization An introduction to nonlinear programming. Models, methods, algorithms, and computer techniques for nonlinear optimization are studied. Students investigate contemporary optimization methods both by implementing these methods and through experimentation with commercial software. Nonmajors wishing to gain practical optimization skills are welcomed in this course. A course project will allow students to explore optimization methods and practical problems directly related to their interests. (Cross listed as DSES-4780. Students cannot obtain credit for both this course and DSES-4780.) Prerequisites: MATH-2010 or ENGR- 1100, and CSCI-1100 or permission of instructor. Spring term annually. 4 credit hours |
| MATP-4940 Readings in Mathematical Programming, Probability, and Mathematical Statistics 1 to 4 credit hours |
| MATP-4960 Topics in Mathematical Programming, Probability, and Mathematical Statistics 1 to 4 credit hours |
| MATP-4980 Undergraduate Project in Mathematical Programming, Probability, and Mathematical Statistics 1 to 4 credit hours |
| MATP-6600 Nonlinear Programming Convex sets and functions, optimality conditions in nonlinear programming, Lagrangian duality, quadratic programming; algorithms for nonlinear programming including Newton’s method, quasi-Newton methods, conjugate gradient methods, together with proofs of convergence. (Cross listed as DSES-6780. Students cannot obtain credit for both this course and DSES-6780.) Prerequisite: MATH-4200 or equivalent or permission of instructor. Fall term annually. 4 credit hours |
| MATP-6620 Combinatorial Optimization and Integer Programming Exact and heuristic methods for solving discrete problems, including the traveling salesman problem, the knapsack problem, packing and covering problems. Algorithm complexity and NP-completeness, cutting plane methods and polyhedral theory, branch and bound, simulated annealing, tabu search, Lagrangian duality. (Cross listed as DSES-6760. Students cannot obtain credit for both this course and DSES-6760.) Prerequisite: MATP-4700 or DSES-4770. Spring term odd-numbered years. 4 credit hours |
| MATP-6640 Linear Programming A unified development of linear systems and linear programming, polyhedral theory, the simplex method, interior point methods, decomposition methods for large-scale linear programming problems, the ellipsoid method, column generation algorithms for stochastic programming, and other problems. (Cross listed as DSES-6770. Students cannot obtain credit for both this course and DSES-6770.) Prerequisites: MATP-4700 or DSES-4770. Spring term even-numbered years. 4 credit hours |
| MATP-6940 Readings in Mathematical Programming, Probability, and Mathematical Statistics 1 to 4 credit hours |
| MATP-6960 Topics in Mathematical Programming, Probability, and Mathematical Statistics 1 to 4 credit hours |
| MATP-6980 Master’s Project Active participation in a Master’s-level project under the supervision of a faculty adviser, leading to a master’s project report. Grades of IP are assigned until the master’s project has been approved by the faculty adviser. If recommended by the adviser, the master’s project may be accepted by the Office of Graduate Education to be archived in the Library. Grades will then be listed as S. 1 to 9 credit hours |
| MATP-6990 Master’s Thesis Active participation in research, under the supervision of a faculty adviser, leading to a master’s thesis. Grades of IP are assigned until the thesis has been approved by the faculty adviser and accepted by the Office of Graduate Education to be archived in a standard format in the library. Grades will then be listed as S. 1 to 9 credit hours |
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Rensselaer Polytechnic Institute (RPI), 110 8th St., Troy, NY 12180. (518) 276-6000 Please direct questions regarding this site to catalog@rpi.edu. |