complexity

[kuh m-plek-si-tee] /kəmˈplɛk sɪ ti/
noun, plural complexities for 2.
1.
the state or quality of being complex; intricacy:
the complexity of urban life.
2.
something complex:
the complexities of foreign policy.
Origin
1715-25; complex + -ity
Related forms
intercomplexity, noun, plural intercomplexities.
overcomplexity, noun
supercomplexity, noun, plural supercomplexities.
Examples from the web for complexity
  • Given the complexity of the issues, none of these questions can yet be answered definitively.
  • Think about your colleagues' lives, in all their complexity.
  • Evolution borrowed those tools for a new task: building bodies of increasing complexity.
  • New research confirms that social complexity enriches cognitive growth.
  • Under the right conditions, patterns emerge from the brain's monumental complexity.
  • complexity researchers who study the behavior of stock markets may have identified a signal that precedes crashes.
  • But neither capture the complexity of what my teaching and intellectual life entails.
  • Rather it's a systematic approach of dealing with complexity.
  • Making sense of the brain's mind-boggling complexity isn't easy.
  • Learning actually increases the brains level of complexity.
British Dictionary definitions for complexity

complexity

/kəmˈplɛksɪtɪ/
noun (pl) -ties
1.
the state or quality of being intricate or complex
2.
something intricate or complex; complication
Word Origin and History for complexity
n.

1721, "composite nature," from complex (adj.) + -ity. Meaning "intricacy" is from 1790. Meaning "a complex condition" is from 1794.

complexity in Technology

algorithm
The level in difficulty in solving mathematically posed problems as measured by the time, number of steps or arithmetic operations, or memory space required (called time complexity, computational complexity, and space complexity, respectively).
The interesting aspect is usually how complexity scales with the size of the input (the "scalability"), where the size of the input is described by some number N. Thus an algorithm may have computational complexity O(N^2) (of the order of the square of the size of the input), in which case if the input doubles in size, the computation will take four times as many steps. The ideal is a constant time algorithm (O(1)) or failing that, O(N).
See also NP-complete.
(1994-10-20)