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Example of a stopping time: a hitting time of Brownian motion.The process starts at 0 and is stopped as soon as it hits 1. In probability theory, in particular in the study of stochastic processes, a stopping time (also Markov time, Markov moment, optional stopping time or optional time [1]) is a specific type of “random time”: a random variable whose value is interpreted as the time at ...
In some cases, however, the user may wish to terminate the algorithm prior to completion. The amount of computation required may be substantial, for example, and computational resources might need to be reallocated. Most algorithms either run to completion or they provide no useful solution information.
Discrete time views values of variables as occurring at distinct, separate "points in time", or equivalently as being unchanged throughout each non-zero region of time ("time period")—that is, time is viewed as a discrete variable. Thus a non-time variable jumps from one value to another as time moves from one time period to the next.
When a Service-End event occurs, the state variable queue-length is decremented by 1 (representing the customer's departure). If the state variable queue-length is still greater than zero, a Service-Start follow-up event is scheduled to happen without any delay. Otherwise, the state variable teller-status is set to "available".
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Graphs of functions commonly used in the analysis of algorithms, showing the number of operations N as the result of input size n for each function. In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm.
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In economics, many theoretical models of the evolution of various economic variables are constructed in continuous time and therefore employ time derivatives. [3]: ch. 1-3 One situation involves a stock variable and its time derivative, a flow variable. Examples include: The flow of net fixed investment is the time derivative of the capital stock.