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Average case : Measure of an algorithm’s performance on typical input data. (2 words)
Worst case : Measure of an algorithm’s performance on the worst possible input. (2 words)
Best case : An algorithm’s performance under optimal conditions. (2 words)
Input : Many programs are extremely sensitive to _____, and performance could fluctuate wildly depending on it.
Constant : If all instructions in a program are executed once, or only a few times, the running time is ________.
Upper bound : One step in algorithm analysis is to find the best _____ _____, which could be achieved for the worst input. (2 words)
Logarithmic : Running time where the program gets slightly slower as N grows; commonly occurs in programs that solve a problem by transforming it into a smaller problem.
Linear : The optimal running time for an algorithm that must process N inputs.
Quadratic : Running time where when N doubles, the running time is squared.
Cubic : Running time where when N doubles, the running time increases eightfold.
Exponential : Running time that is some value to the power of N.
Big Oh : The notation used to show worst case performance.
Algorithm analysis : The determination of the amount of resources (time and storage) needed to execute an algorithm. (2 words)
Efficiency : Relationship between input length and number of steps for an algorithm.
Largest : When the results of algorithm analysis are an expression consisting of a sequence of decreasing terms (n^2 + n + 1) we care most about the _______ term.
uniform : The _______ cost model assigns a constant cost to every machine operation, regardless of the size of the numbers involved.
Asymptotic : A type of analysis which estimates an upper bound for an algorithm based on its complexity for arbitrarily large input.
Increases : As input size increases, the running time of an algorithm _________.
Memory : The methods of run-time analysis can also be used for other growth rates, such as ______ usage.
Amortized : A type of analysis that considers the entire sequence of operations, not just those handling input/output, and targets the worst case scenario.
Polynomial : An algorithm is said to be of __________ time if its running time is upper bounded by a polynomial expression in the size of the input for the algorithm.
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- The notation used to show worst case performance.
- Running time where when N doubles, the running time increases eightfold.
- Many programs are extremely sensitive to _____, and performance could fluctuate wildly depending on it.
- The optimal running time for an algorithm that must process N inputs.
- The methods of run-time analysis can also be used for other growth rates, such as ______ usage.
- When the results of algorithm analysis are an expression consisting of a sequence of decreasing terms (n^2 + n + 1) we care most about the _______ term.
- The _______ cost model assigns a constant cost to every machine operation, regardless of the size of the numbers involved.
- An algorithm’s performance under optimal conditions. (2 words)
- If all instructions in a program are executed once, or only a few times, the running time is ________.
- A type of analysis that considers the entire sequence of operations, not just those handling input/output, and targets the worst case scenario.
- As input size increases, the running time of an algorithm _________.
| - Running time where when N doubles, the running time is squared.
- Measure of an algorithm’s performance on the worst possible input. (2 words)
- A type of analysis which estimates an upper bound for an algorithm based on its complexity for arbitrarily large input.
- Relationship between input length and number of steps for an algorithm.
- An algorithm is said to be of __________ time if its running time is upper bounded by a polynomial expression in the size of the input for the algorithm.
- One step in algorithm analysis is to find the best _____ _____, which could be achieved for the worst input. (2 words)
- Measure of an algorithm’s performance on typical input data. (2 words)
- Running time that is some value to the power of N.
- Running time where the program gets slightly slower as N grows; commonly occurs in programs that solve a problem by transforming it into a smaller problem.
- The determination of the amount of resources (time and storage) needed to execute an algorithm. (2 words)
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PuzzleFast.com, Noncommercial Use Only
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- BIGOH
- CUBIC
- INPUT
- LINEAR
- MEMORY
- LARGEST
- UNIFORM
- BESTCASE
- CONSTANT
- AMORTIZED
- INCREASES
| - QUADRATIC
- WORSTCASE
- ASYMPTOTIC
- EFFICIENCY
- POLYNOMIAL
- UPPERBOUND
- AVERAGECASE
- EXPONENTIAL
- LOGARITHMIC
- ALGORITHMANALYSIS
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© 2013
PuzzleFast.com, Noncommercial Use Only