In mathematics, a function f(x) between two ordered sets is unimodal if for some value m (the mode), it is monotonically increasing for x ≤ m and monotonically decreasing for x ≥ m. In that case, the maximum value of f(x) is f(m) and there are no other local maxima.
Examples of unimodal functions:
- Pascal's triangle, each row.
- Quadratic polynomial with a negative quadratic coefficient
- Logistic map
- Tent map
Function 
is "S-unimodal" if its Schwartzian derivative is negative for all 
.
In probability and statistics, a "unimodal probability distribution" is a probability distribution whose probability density function is a unimodal function, or more generally, whose cumulative distribution function is convex up to m and concave thereafter (this allows for the possibility of a non-zero probability for x=m). For a unimodal probability distribution of a continuous random variable, the Vysochanskii-Petunin inequality provides a refinement of the Chebyshev inequality. Compare multimodal distribution.
In computational geometry if a function is unimodal it permits the design of efficient algorithms for finding the extrema of the function[1]
- ^ Godfried T. Toussaint, "Complexity, convexity, and unimodality," International Journal of Computer and Information Sciences, Vol. 13, No. 3, June 1984, pp. 197-217.
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