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Average performance and worst-case performance are the most used in algorithm analysis. Less widely found is best-case performance, but it does have uses: for example, where the best cases of individual tasks are known, they can be used to improve the accuracy of an overall worst-case analysis.
There are several broadly recognized algorithmic techniques that offer a proven method or process for designing and constructing algorithms. Different techniques may be used depending on the objective, which may include searching, sorting, mathematical optimization, constraint satisfaction, categorization, analysis, and prediction.
The critical path method (CPM), or critical path analysis (CPA), is an algorithm for scheduling a set of project activities. [1] A critical path is determined by identifying the longest stretch of dependent activities and measuring the time [ 2 ] required to complete them from start to finish.
PICK charts are a method to prioritize a number of action items or problem solving ideas. A pick chart allows visual comparison of action items relative to their impact to the problem being addressed vs. the ease/cost of implementation. In VERY rudimentary terms, PICK charts are a Return On Investment (ROI) method.
In computer science, the analysis of algorithms is the process of finding the computational complexity of algorithms—the amount of time, storage, or other resources needed to execute them. Usually, this involves determining a function that relates the size of an algorithm's input to the number of steps it takes (its time complexity ) or the ...
Identify creative solutions to eliminate the key root causes in order to fix and prevent process problems. One can use brainstorming or techniques like six thinking hats and random word. Some projects can utilize complex analysis tools like design of experiments (DOE), but try to focus on obvious solutions if these are apparent. However, the ...
Algorithm engineering focuses on the design, analysis, implementation, optimization, profiling and experimental evaluation of computer algorithms, bridging the gap between algorithmics theory and practical applications of algorithms in software engineering. [1]
In computer science, algorithmic efficiency is a property of an algorithm which relates to the amount of computational resources used by the algorithm. Algorithmic efficiency can be thought of as analogous to engineering productivity for a repeating or continuous process. For maximum efficiency it is desirable to minimize resource usage.
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