Mathematical optimization

Graph of a surface given by z = f(x, y) = −(x² + y²) + 4. The global maximum at (x, y, z) = (0, 0, 4) is indicated by a blue dot.
Nelder-Mead minimum search of Simionescu's function. Simplex vertices are ordered by their values, with 1 having the lowest ( best) value.

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives.[1][2] It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering[3] to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.[4][5]

In the more general approach, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics.[6]

  1. ^ "The Nature of Mathematical Programming Archived 2014-03-05 at the Wayback Machine," Mathematical Programming Glossary, INFORMS Computing Society.
  2. ^ "Mathematical Programming: An Overview" (PDF). Retrieved 26 April 2024.
  3. ^ Martins, Joaquim R. R. A.; Ning, Andrew (2021-10-01). Engineering Design Optimization. Cambridge University Press. ISBN 978-1108833417.
  4. ^ Du, D. Z.; Pardalos, P. M.; Wu, W. (2008). "History of Optimization". In Floudas, C.; Pardalos, P. (eds.). Encyclopedia of Optimization. Boston: Springer. pp. 1538–1542.
  5. ^ "Mathematical optimization". Engati. Retrieved 2024-08-24.
  6. ^ "Open Journal of Mathematical Optimization". ojmo.centre-mersenne.org. Retrieved 2024-08-24.