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Graphs of maps, especially those of one variable such as the logistic map, are key to understanding the behavior of the map. One of the uses of graphs is to illustrate fixed points, called points. Draw a line y = x (a 45° line) on the graph of the map. If there is a point where this 45° line intersects with the graph, that point is a fixed point.
Logistic equation can refer to: Logistic map, a nonlinear recurrence relation that plays a prominent role in chaos theory; Logistic regression, a regression technique that transforms the dependent variable using the logistic function; Logistic differential equation, a differential equation for population dynamics proposed by Pierre François ...
The standard logistic function is the logistic function with parameters =, =, =, which yields = + = + = / / + /.In practice, due to the nature of the exponential function, it is often sufficient to compute the standard logistic function for over a small range of real numbers, such as a range contained in [−6, +6], as it quickly converges very close to its saturation values of 0 and 1.
For a given iterated function :, the plot consists of a diagonal (=) line and a curve representing = ().To plot the behaviour of a value , apply the following steps.. Find the point on the function curve with an x-coordinate of .
A particular case of the generalised logistic function is: = (+ ()) /which is the solution of the Richards's differential equation (RDE): ′ = (()) with initial condition
As the amounts of collected data and computing power grow (multicore, GPUs, clusters, clouds), modern datasets no longer fit into one computing node. Efficient distributed parallel algorithms for handling large-scale data are required. The GraphLab framework is a parallel programming abstraction targeted for sparse iterative graph
A sigmoid function is any mathematical function whose graph has a characteristic S-shaped or sigmoid curve. A common example of a sigmoid function is the logistic function, which is defined by the formula: [1] = + = + = ().
A graph of the logistic function on the t-interval (−6,6) is shown in Figure 1. Let us assume that t {\displaystyle t} is a linear function of a single explanatory variable x {\displaystyle x} (the case where t {\displaystyle t} is a linear combination of multiple explanatory variables is treated similarly).