Simulated Annealing for Convex Optimization

Published Online:https://doi.org/10.1287/moor.1060.0194

References

  • Applegate D., Kannan R. Sampling and integration of near logconcave functions. Proc. Annual Sympos. Theory Comput. (1990) 156–163Google Scholar
  • Bertsimas D., Vempala S. Solving convex programs by random walks. J. ACM (2004) 51:540–556CrossrefGoogle Scholar
  • Cover T., Thomas J.Elements of Information Theory (1991) (Wiley, New York) CrossrefGoogle Scholar
  • Dinghas A. Uber eine Klasse superadditiver Mengenfunktionale von Brunn–Minkowski–Lusternik-schem Typus. Math. Zeitschrift (1957) 68:111–125CrossrefGoogle Scholar
  • Frieze A., Kannan R., Polson N. Sampling from logconcave distributions. Ann. Appl. Probab. (1994) 4:812–837Correction: Sampling from logconcave distributions. 1994. Ann. Appl. Probab. 4 1255CrossrefGoogle Scholar
  • Gilks W., Wild P. Adaptive rejection sampling for Gibbs sampling. Appl. Statist. (1992) 41:337–348CrossrefGoogle Scholar
  • Grötchel L., Lovász A., Schrijver L.Geometric Algorithms and Combinatorial Optimization (1988) (Springer-Verlag, Berlin, Germany) CrossrefGoogle Scholar
  • Hajek B. Cooling schedules for optimal annealing. Math. Oper. Res. (1988) 13:311–329LinkGoogle Scholar
  • Jerrum M., Sorkin G. Simulated annealing for graph bisection. Proc. Annual IEEE Sympos. Foundations Comput. Sci. (1993) 94–103Google Scholar
  • Jerrum M., Sinclair A., Vigoda E. A polynomial-time approximation algorithm for the permanent of a matrix with non-negative entries. Proc. Annual Sympos. Theory Comput. (2001) 712–721Google Scholar
  • Kannan R., Lovász L., Simonovits M. Random walks and an O*(n5) volume algorithm for convex bodies. Random Structures Algorithms (1997) 11:1–50CrossrefGoogle Scholar
  • Kirkpatrick S., Gelatt C. D., Vecchi M. P. Optimization by simulated annealing. Science (1983) 220:671–680CrossrefGoogle Scholar
  • Leindler L. On a certain converse of Hölder’s Inequality II. Acta Sci. Math. Szeged (1972) 33:217–223Google Scholar
  • Lovász L., Vempala S.The Geometry of Logconcave Functions and Sampling Algorithms (2003) . (Preliminary version in 2003. Proc. Annual IEEE Sympos. Foundations Comput. Sci., 650–659.) http://math.mit.edu/∼vempala/papers/logcon.pdfGoogle Scholar
  • Lovász L., Vempala S. Hit-and-run from a corner. SIAM J. Comput. (2006) 35(4):985–1005CrossrefGoogle Scholar
  • Lovász L., Vempala S. Simulated annealing in convex bodies and an O*(n4) volume algorithm. J. Comput. System Sci. (2006) 72(2):392–417CrossrefGoogle Scholar
  • Prékopa A. Logarithmic concave measures and functions. Acta Sci. Math. Szeged (1973) 34:335–343Google Scholar
  • Prékopa A. On logarithmic concave measures with applications to stochastic programming. Acta Sci. Math. Szeged (1973) 32:301–316Google Scholar
  • Press W., Flannery B., Teukolsky S., Vetterling W.Numerical Recipes in C: The Art and Science of Computing (1992) 2nd ed.(Cambridge University Press, Cambridge, UK) Google Scholar
  • Rudelson M. Random vectors in the isotropic position. J. Funct. Anal. (1999) 164:60–72CrossrefGoogle Scholar
  • Sorkin G. Efficient simulated annealing on fractal energy landscapes. Algorithmica (1991) 6:367–418CrossrefGoogle Scholar
  • Spall J.Introduction to Stochastic Search and Optimization (2003) (Wiley, New York) CrossrefGoogle Scholar
  • Vempala S. Geometric random walks: A survey. MSRI Volume on Combinatorial and Computational Geometry http://www-math.mit.edu/∼vempala/survey.psGoogle Scholar
  • Zabinsky Z. B.Stochastic Adaptive Search for Global Optimization (2003) (Kluwer Academic Publishers, Boston, MA) CrossrefGoogle Scholar
  • Zabinsky Z. B., Smith R. L., McDonald J. F., Romeijn H. E., Kaufman D. E. Improving hit-and-run for global optimization. J. Global Optim. (1993) 3:171–192CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.