arithmetic complexity - Definition. Was ist arithmetic complexity
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Was (wer) ist arithmetic complexity - definition

Programming Complexity; Software complexity; Code complexity

Arithmetic circuit complexity         
In computational complexity theory, arithmetic circuits are the standard model for computing polynomials. Informally, an arithmetic circuit takes as inputs either variables or numbers, and is allowed to either add or multiply two expressions it has already computed.
Computational complexity         
MEASURE OF THE AMOUNT OF RESOURCES NEEDED TO RUN AN ALGORITHM OR SOLVE A COMPUTATIONAL PROBLEM
Asymptotic complexity; Computational Complexity; Bit complexity; Context of computational complexity; Complexity of computation (bit); Computational complexities
In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements.
complexity         
PROFESSIONAL ESPORTS ORGANIZATION BASED IN THE UNITED STATES
Los Angeles Complexity; CompLexity Gaming; LA Complexity; Complexity LA; CompLexity; Team CompLexity; CoL.Black; CoL
<algorithm> The level in difficulty in solving mathematically posed problems as measured by the time, number of steps or arithmetic operations, or memory space required (called time complexity, computational complexity, and space complexity, respectively). The interesting aspect is usually how complexity scales with the size of the input (the "scalability"), where the size of the input is described by some number N. Thus an algorithm may have computational complexity O(N^2) (of the order of the square of the size of the input), in which case if the input doubles in size, the computation will take four times as many steps. The ideal is a constant time algorithm (O(1)) or failing that, O(N). See also NP-complete. (1994-10-20)

Wikipedia

Programming complexity

Programming complexity (or software complexity) is a term that includes many properties of a piece of software, all of which affect internal interactions. According to several commentators, there is a distinction between the terms complex and complicated. Complicated implies being difficult to understand but with time and effort, ultimately knowable. Complex, on the other hand, describes the interactions between a number of entities. As the number of entities increases, the number of interactions between them would increase exponentially, and it would get to a point where it would be impossible to know and understand all of them. Similarly, higher levels of complexity in software increase the risk of unintentionally interfering with interactions and so increases the chance of introducing defects when making changes. In more extreme cases, it can make modifying the software virtually impossible. The idea of linking software complexity to the maintainability of the software has been explored extensively by Professor Manny Lehman, who developed his Laws of Software Evolution from his research. He and his co-author Les Belady explored numerous possible Software Metrics in their oft-cited book, that could be used to measure the state of the software, eventually reaching the conclusion that the only practical solution would be to use one that uses deterministic complexity models.