Article · Wikipedia archive · Last revised Jun 10, 2026

Sphere function

In mathematical optimization, the sphere function is a convex function used as a performance test problem for optimization algorithms. The sphere function was proposed by Kenneth A. De Jong in 1975 as the first item of a series of computational test sets. Because of this, the sphere function is also collectively referred to as De Jong's function or De Jong's first function.

Last revised
Jun 10, 2026
Read time
≈ 1 min
Length
312 w
Citations
6
Source
Sphere function of two variables
Contour plot of the sphere function
Contour

In mathematical optimization, the sphere function is a convex function used as a performance test problem for optimization algorithms. The sphere function was proposed by Kenneth A. De Jong in 1975 as the first item of a series of computational test sets.1 Because of this, the sphere function is also collectively referred to as De Jong's function or De Jong's first function.2

On a n {\displaystyle n} -dimensional domain it is defined by f ( ( x 1 , x 2 , , x n ) ) = i = 1 n x i 2 . {\displaystyle f((x_{1},x_{2},\dots ,x_{n}))=\sum _{i=1}^{n}x_{i}^{2}.}

The function is typically evaluated on the domain x i [ 5.12 , 5.12 ] {\displaystyle x_{i}\in [-5.12,5.12]} for all 1 i n {\displaystyle 1\leq i\leq n} .2

It has a global minimum of zero at x i = 0. {\displaystyle x_{i}=0.} It is a separable function; that is, it can be expressed as a product of functions in one variable.3

The sphere function is used as a benchmark problem to measure algorithms' precision, convergence rate, and robustness, specifically over how well the algorithm handles the function's smooth nature. Several variants of the sphere function are also used, including the Rastrigin function.24

See also

See also

References

References

  1. De Jong, Kenneth Alan (1975). An analysis of the behavior of a class of genetic adaptive systems (PhD thesis). University of Michigan.
  2. Molga, Marcin; Smutnicki, Czesław (2005), Test functions for optimization needs (PDF), Robert Marks, p. 2
  3. Jamil, Momin; Yang, Xin She (2013). "A literature survey of benchmark functions for global optimisation problems". International Journal of Mathematical Modelling and Numerical Optimisation. 4 (2) 55204: 150. arXiv:1308.4008. doi:10.1504/IJMMNO.2013.055204.
  4. Picheny, Victor; Wagner, Tobias; Ginsbourger, David (2013). "A benchmark of kriging-based infill criteria for noisy optimization". Structural and Multidisciplinary Optimization. 48 (3): 607–626. doi:10.1007/s00158-013-0919-4.