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Biological exponential growth is the unrestricted growth of a population of organisms, occurring when resources in its habitat are unlimited. [1] Most commonly apparent in species that reproduce quickly and asexually , like bacteria , exponential growth is intuitive from the fact that each organism can divide and produce two copies of itself.
Variations on the Traveling salesman problem. The problem for graphs is NP-complete if the edge lengths are assumed integers. The problem for points on the plane is NP-complete with the discretized Euclidean metric and rectilinear metric. The problem is known to be NP-hard with the (non-discretized) Euclidean metric. [3]: ND22, ND23
In extremal graph theory, the forbidden subgraph problem is the following problem: given a graph , find the maximal number of edges (,) an -vertex graph can have such that it does not have a subgraph isomorphic to .
The Hayflick limit, or Hayflick phenomenon, is the number of times a normal somatic, differentiated human cell population will divide before cell division stops. [ 1 ] [ 2 ] The concept of the Hayflick limit was advanced by American anatomist Leonard Hayflick in 1961, [ 3 ] at the Wistar Institute in Philadelphia , Pennsylvania.
In graph theory and statistics, a graphon (also known as a graph limit) is a symmetric measurable function : [,] [,], that is important in the study of dense graphs. Graphons arise both as a natural notion for the limit of a sequence of dense graphs, and as the fundamental defining objects of exchangeable random graph models.
The weighted version of the decision problem was one of Karp's 21 NP-complete problems; [11] Karp showed the NP-completeness by a reduction from the partition problem. The canonical optimization variant of the above decision problem is usually known as the Maximum-Cut Problem or Max-Cut and is defined as: Given a graph G, find a maximum cut.
This is a list of limits for common functions such as elementary functions. In this article, the terms a , b and c are constants with respect to x . Limits for general functions
The equation for figure 2 is the differential of equation 1.1 (Verhulst's 1838 growth model): [13] = (equation 1.2) can be understood as the change in population (N) with respect to a change in time (t). Equation 1.2 is the usual way in which logistic growth is represented mathematically and has several important features.