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Mathematical Decision-Making with Linear and Convex Programming

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A Primer for Undergraduate Research

Part of the book series: Foundations for Undergraduate Research in Mathematics ((FURM))

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Abstract

Operations research (OR) is a branch of applied mathematics dedicated to analytical decision-making, and has close ties with optimization, statistics, and mathematical modeling. As a discipline, it is often housed in schools of engineering or business, and undergraduate students of mathematics may have little exposure to the field. However, for students interested in practical applications of mathematics, operations research contains a wonderful abundance of problems. In this brief introduction, we discuss two classes of optimization problems of interest to operations research, linear programs (LP) and their generalization, convex programs. Both theory and applications are outlined. We also mention project ideas that could be pursued by undergraduates.

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References

  1. Adler, I., Megiddo, N., Todd, M.J.: New results on the average behavior of simplex algorithms. Bull. Am. Math. Soc. 11(2), 378–382 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bertsimas, D., Tsitsiklis, J.N.: Introduction to Linear Optimization. Athena Scientific, Belmont (1997)

    Google Scholar 

  3. Bosch, R.A.: The battle of the burger chains: which is best, burger king, mcdonald’s, or wendy’s? Socio Econ. Plan. Sci. 30(3), 157–162 (1996)

    Article  Google Scholar 

  4. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  5. Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 2.1. http://cvxr.com/cvx, Mar (2014)

  6. Gurobi optimization. http://www.gurobi.com/products/gurobi-optimizer

  7. IBM ILOG cplex optimization studio community edition. https://www-01.ibm.com/software/websphere/products/optimization/cplex-studio-community-edition

  8. Klee, V., Minty, G.J.: How Good is the Simplex Algorithm? Defense Technical Information Center (1970)

    Google Scholar 

  9. Lobo, M.S., Vandenberghe, L., Boyd, S., Lebret, H.: Applications of second-order cone programming. Linear Algebra Appl. 284, 193–228 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  10. MathWorks.: Matlab optimization toolbox. http://www.mathworks.com/products/optimization

  11. Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer, Berlin (2006)

    MATH  Google Scholar 

  12. Shamir, R.: The efficiency of the simplex method: a survey. Manag. Sci. 33(3), 301–34 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  13. Smale, S.: Mathematical problems for the next century. Math. Intell. 20(2), 7–15 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  14. Weinberger, K.Q., Saul, L.K.: Unsupervised learning of image manifolds by semidefinite programming. Proc. 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2004)

    Google Scholar 

  15. Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. Machine Lear. Res. 10, 207–244 (2009)

    MATH  Google Scholar 

  16. Wolkowicz, H., Saigal, R., Vandenberghe, L. (eds.): Handbook of Semidefinite Programming. Kluwer Academic Publishers, Boston (2000)

    MATH  Google Scholar 

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Correspondence to Jakob Kotas .

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Kotas, J. (2017). Mathematical Decision-Making with Linear and Convex Programming. In: Wootton, A., Peterson, V., Lee, C. (eds) A Primer for Undergraduate Research. Foundations for Undergraduate Research in Mathematics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-66065-3_8

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