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Qalculate! supports common mathematical functions and operations, multiple bases, autocompletion, complex numbers, infinite numbers, arrays and matrices, variables, mathematical and physical constants, user-defined functions, symbolic derivation and integration, solving of equations involving unknowns, uncertainty propagation using interval arithmetic, plotting using Gnuplot, unit and currency ...
mXparser is an open-source mathematical expressions parser/evaluator providing abilities to calculate various expressions at a run time. [1] Expressions definitions are given as plain text, then verified in terms of grammar / syntax, finally calculated.
The decorator pattern is a design pattern used in statically-typed object-oriented programming languages to allow functionality to be added to objects at run time; Python decorators add functionality to functions and methods at definition time, and thus are a higher-level construct than decorator-pattern classes.
In computer science, amortized analysis is a method for analyzing a given algorithm's complexity, or how much of a resource, especially time or memory, it takes to execute. The motivation for amortized analysis is that looking at the worst-case run time can be too pessimistic.
Then we are told that the 1st part is not sped up, so s1 = 1, while the 2nd part is sped up 5 times, so s2 = 5, the 3rd part is sped up 20 times, so s3 = 20, and the 4th part is sped up 1.6 times, so s4 = 1.6. By using Amdahl's law, the overall speedup is
The running time of LPT is dominated by the sorting, which takes O(n log n) time, where n is the number of inputs. LPT is monotone in the sense that, if one of the input numbers increases, the objective function (the largest sum or the smallest sum of a subset in the output) weakly increases. [2] This is in contrast to Multifit algorithm.
The data from the run is written to several coverage data files with the extensions ‘.bb’ ‘.bbg’ and ‘.da’ respectively in the current directory. If the program execution varies based on the input parameters or data, it can be run multiple times and the results will accumulate in the coverage data files for overall analysis.
Informally, this means that the running time increases at most linearly with the size of the input. More precisely, this means that there is a constant c such that the running time is at most for every input of size n. For example, a procedure that adds up all elements of a list requires time proportional to the length of the list, if the ...