Introduction
Even with the success of the simplex method for linear programming (LP), there was from the earliest days of operations research a desire to create an algorithm for solving LP problems that proceeded on a path through the polytope rather than around its perimeter. Interior point methods (IPMs) were first developed in 1950s, analyzed and first implemented in the 1960s. At that time the conclusion was made that IPMs were not competitive with other algorithms, especially with simplex methods. The continuous effort to find a polynomial algorithm for LP problems led to the revitalization of IPMs. In 1984 Karmarkar first proved the polynomial complexity of an IPM, which led to the “Interior Point Revolution” (Wright 2004) in mathematical programming. In this article the motivation for desiring an interior path, the concept of the complexity of solving LP problems, a brief history of the developments in the area, and the research state of the art are discussed, including...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andersen, E. D., Roos, C., & Terlaky, T. (2003). On implementing a primal–dual interior–point method for conic quadratic optimization. Mathematical Programming, 95(2), 249–277.
Ben-Tal, A., & Nemirovski, A. (2001). Lectures on modern convex optimization: Analysis, algorithms, and engineering applications (MPS-SIAM series on optimization). Philadelphia, PA: SIAM.
Bixby, R. E. (2002). Solving real-world linear programs: A decade and more of progress. Operations Research, 50(1), 3–15.
Bomze, I. M., Duerr, M., de Klerk, E., Roos, C., Quist, A. J., & Terlaky, T. (2000). On copositive programming and standard quadratic optimization problems. Journal of Global Optimization, 18(2), 301–320.
Borgwardt, K. H. (1987). The simplex method: A probabilistic analysis, algorithms and combinatorics (Vol. 1). Berlin: Springer.
Byrd, R., Nocedal, J., & Waltz, R. (2006). KNITRO: An integrated package for nonlinear optimization. In G. Di Pillo & M. Roma (Eds.), Large-scale nonlinear optimization (Nonconvex optimization and its applications, Vol. 83, pp. 35–59). Berlin: Springer.
Chu, M., Zinchenko, Y., Henderson, S. G., & Sharpe, M. B. (2008). Robust optimization for intensity modulated radiation therapy treatment planning under uncertainty. Physics in Medicine and Biology, 53, 3231–3250.
Craig, T., Sharpe, M. B., Terlaky, T., & Zinchenko, Y. (2008). Controlling the dose distribution with gEUD-type constraints within the convex IMRTP framework. Physics in Medicine and Biology, 53, 3231–3250.
de Klerk, E. (2002). Aspects of semidefinite programming: Interior point algorithms and selected applications. Dordrecht, The Netherlands: Kluwer.
den Hertog, D. (1994). Interior point approach to linear, quadratic and convex programming. Dordrecht, The Netherlands: Kluwer.
Deza, A., Nematollahi, E., Peyghami, R., & Terlaky, T. (2006). The central path visits all the vertices of the Klee-Minty cube. Optimization Methods and Software, 21, 851–865.
Deza, A., Nematollahi, E., & Terlaky, T. (2008). How good are interior point methods? Klee-Minty cubes tighten iteration-complexity bounds. Mathematical Programming, 113, 1–14.
Deza, A., Terlaky, T., & Zinchenko, Y. (2008). Polytopes and arrangements: Diameter and curvature. Operations Research Letters, 36, 215–222.
Deza, A., Terlaky, T., & Zinchenko, Y. (2009). The continuous d-step conjecture for polytopes. Discrete and Computational Geometry, 41, 318–327.
Dikin, I. I. (1967). Iterative solution of problems of linear and quadratic programming. Soviet Mathematics Doklady, 8, 674–675.
Fiacco, A. V., & McCormick, G. P. (1968). Nonlinear programming: Sequential unconstrained minimization techniques. New York: John Wiley.
Frish, K. R. (1954). Principles of linear programming – the double gradient form of the logarithmic potential method. Memorandum, Institute of Economics, University of Oslo, Oslo, Norway.
Ghaffari Hadigheh, A. R., Romanko, O., & Terlaky, T. (2007). Sensitivity analysis in convex quadratic optimization: Simultaneous perturbation of the objective and right-hand-side vectors. Algorithmic Operations Research, 2(2), 4–111.
Goldfarb, D., & Todd, M. J. (1989). Linear programming. In G. L. Nemhauser, A. H. G. Rinnooy Kan, & M. J. Todd (Eds.), Optimization (pp. 73–170). Amsterdam/New York: North Holland.
Gonzaga, C. C. (1991a). Large-steps path-following methods for linear programming, part I: Barrier function method. SIAM Journal on Optimization, 1, 268–279.
Gonzaga, C. C. (1991b). Large-steps path-following methods for linear programming, part II: Potential reduction method. SIAM Journal on Optimization, 1, 280–292.
Gonzaga, C. C. (1992). Path following methods for linear programming. SIAM Review, 34, 167–224.
Huard, P. (1967). Resolution of mathematical programming with nonlinear constraints by the method of centres. In J. Abadie (Ed.), Nonlinear programming (pp. 209–219). Amsterdam: North Holland.
Illés, T., Peng, J., Roos, C., & Terlaky, T. (2000). A strongly polynomial rounding scheme in interior point methods for P*(κ) linear complementarity problems. SIAM Journal on Optimization, 11(2), 320–340.
Illés, T., & Terlaky, T. (2002). Pivot versus interior point methods: Pros and cons. European Journal of Operational Research, 140(2), 6–26.
Jansen, B. (1997). Interior point techniques in ptimization. Complexity, sensitivity and algorithms. Dordrecht, The Netherlands: Kluwer.
Karmarkar, N. K. (1984). A new polynomial-time algorithm for linear programming. Combinatorica, 4, 373–395.
Khachiyan, L. G. (1979). A polynomial algorithm in linear programming. Translated in Soviet Mathematics Doklady, 20, 191–194.
Klee, V. & Minty, G. J. (1972). How good is the simplex algorithm. In O. Shisha (Ed.), Inequalities III (pp. 159–175). Academic Press.
Kojima, M., Megiddo, N., Noma, T., & Yoshise, A. (1991). A unified approach to interior point al − go − rithms for linear complementarity problems (Lecture notes in computer science, Vol. 538). Berlin, Germany: Springer.
Koltai, T., & Terlaky, T. (2000). The difference between managerial and mathematical interpretation of sensitivity analysis results in linear programming. International Journal of Production Economics, 65, 257–274.
Nematollahi, E., & Terlaky, T. (2008). A simpler and tighter redundant Klee-Minty construction. Optimization Letters, 2(3), 403–414.
Nesterov, Y. E., & Nemirovskii, A. S. (1994). Interior point polynomial methods in convex programming: Theory and algorithms. Philadelphia: SIAM.
Peng, J., Roos, C., & Terlaky, T. (2002). Self-regularity: A new paradigm for primal-dual interior-point algorithms. Princeton, NJ: Princeton University Press.
Roos, C., & Terlaky, T. (1997). Advances in linear optimization. In M. DellAmico, F. Maffioli, & S. Martello (Eds.), Annotated bibliography in combinatorial optimization, Chap − ter 7. New York: John Wiley & Sons.
Roos, C., Terlaky, G. J., & Vial J. -Ph. (1997). Interior point methods for linear optimization. (New York: Springer, 2nd ed., 2006). (Roos, C., Terlaky, T., Vial, J. -Ph. (1997). Theory and algorithms for linear optimization: An interior point approach. Chichester, UK: John Wiley & Sons).
Santos, F. (2010). A counterexample to the Hirsch conjecture. arXiv:1006.2814.
Sonnevend, G. y. (1985). An ‘analytic center’ for polyhedrons and new classes of global algorithms for linear (smooth, convex) programming. In A. Prékopa, J. Szelezsán, & B. Strazicky (Eds.), System modeling and optimization : Proceedings of the 12th IFIP-Conference held in Budapest, Hungary, September 1985. Lecture notes in control and information sciences (Vol. 84, pp. 866–876). Berlin, West–Germany: Springer Verlag, 1986.
Terlaky, T. (Ed.). (1996). Interior point methods in mathematical programming. Dordrecht, The Netherlands: Kluwer.
Todd, M. (1999). A study of search directions in primal-dual interior-point methods for semidefinite programming. Optimization Methods and Software, 11, 1–46.
Vandenberghe, L., & Boyd, S. (1996). Semidefinite programming. SIAM Review, 38, 49–95.
Wächter, A. (2002). An interior point algorithm for large-scale nonlinear optimization with applications in process engineering. Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA, USA.
Wächter, A., & Biegler, L. T. (2006). On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming. Mathematical Programming, 106(1), 25–57.
Wolkowicz, H., Saigal, R., Vandenberghe, L. (Eds.) (2000). Handbook of semidefinite programming: Theory, algorithms, and applications. Kluwer A.P.C.
Wright, S. J. (1996). Primal-dual interior-point methods. Philadelphia: SIAM.
Wright, M. H. (2004). The interior-point revolution in optimization: History, recent developments, and lasting consequences. Bulletin (New Series) of the American Mathematical Society, 42(1): 39–56.
Ye, Y. (1997). Interior point algorithms. New York: John Wiley & Sons.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this entry
Cite this entry
Terlaky, T., Boggs, P.T. (2013). Interior-Point Methods for Conic-Linear Optimization. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_475
Download citation
DOI: https://doi.org/10.1007/978-1-4419-1153-7_475
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-1137-7
Online ISBN: 978-1-4419-1153-7
eBook Packages: Business and Economics