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Given a transformation between input and output values, described by a mathematical function, optimization deals with generating and selecting the best solution from some set of available alternatives, by systematically choosing input values from within an allowed set, computing the output of the function and recording the best output values found during the process.
PIDO stands for Process Integration and Design Optimization.Process Integration is needed as many software tools are used in a multi-domain system design. Control software is developed in a different toolchain than the mechanical properties of a system, where structural analysis is done using again some different tools.
In computer science, program optimization, ... or software optimization is the process of modifying ... As an example, caching data at the application level that is ...
Profiling results can be used to guide the design and optimization of an individual algorithm; the Krauss matching wildcards algorithm is an example. [5] Profilers are built into some application performance management systems that aggregate profiling data to provide insight into transaction workloads in distributed applications. [6]
Process optimization is the discipline of adjusting a process so as to make the best or most effective use of some specified set of parameters without violating some constraint. Common goals are minimizing cost and maximizing throughput and/or efficiency. Process optimization is one of the major quantitative tools in industrial decision making.
The goal of a process simulation is to find optimal conditions for a process. This is essentially an optimization problem which has to be solved in an iterative process. In the example above the feed stream to the column is defined in terms of its chemical and physical properties.
The optimization software will deliver input values in A, the software module realizing f will deliver the computed value f(x). In this manner, a clear separation of concerns is obtained: different optimization software modules can be easily tested on the same function f, or a given optimization software can be used for different functions f.
Examples of these kinds of methods include tabu search and genetic algorithms. [4] Metamodels enable researchers to obtain reliable approximate model outputs without running expensive and time-consuming computer simulations. Therefore, the process of model optimization can take less computation time and cost. [8]
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