# Stochastic Simulation Optimization by Chun-Hung Chen download in pdf, ePub, iPad

The former runs a presimulation to estimate the firing frequency of reactions, whereas the latter sorts the cumulative array on-the-fly. This dependency graph tells which reaction propensities to update after a particular reaction has fired. Reduces the computational cost to constant time i. Use factored-out, partial reaction propensities to reduce the computational cost to scale with the number of species in the network, rather than the larger number of reactions.

Next, the cumulative sum of the array is taken, and the final cell contains the number R, where R is the total event rate. The use of partial-propensity methods is limited to elementary chemical reactions, i. Continuous simulation thereby simulates the system over time, given differential equations determining the rates of change of state variables.

Limits the outcomes where the variable can only take on discrete values. To make the sampling of reactions more efficient, an indexed priority queue is used to store the reaction times. The following methods can dramatically improve simulation speed by some approximations. On the other hand, to make the recomputation of propensities more efficient, a dependency graph is used.

Every non-elementary chemical reaction can be equivalently decomposed into a set of elementary ones, at the expense of a linear in the order of the reaction increase in network size. This is an improvement over the first reaction method where the unused reaction times are reused.

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