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In questa pagina puoi ottenere un'analisi dettagliata di una parola o frase, prodotta utilizzando la migliore tecnologia di intelligenza artificiale fino ad oggi:
- come viene usata la parola
- frequenza di utilizzo
- è usato più spesso nel discorso orale o scritto
- opzioni di traduzione delle parole
- esempi di utilizzo (varie frasi con traduzione)
- etimologia
Cosa (chi) è communication complexity - definizione
Communication complexity
COMPLEXITY OF SENDING INFORMATION IN A DISTRIBUTED ALGORITHM
Quantum communication complexity
In theoretical computer science, communication complexity studies the amount of communication required to solve a problem when the input to the problem is distributed among two or more parties. The study of communication complexity was first introduced by Andrew Yao in 1979, while studying the problem of computation distributed among several machines.
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)
