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note01 - Outline 1 Algorithm Analysis Growth rate functions...

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Outline 1 Algorithm Analysis 2 Growth rate functions 3 The properties of growth rate functions: 4 Importance of the growth rate 5 An example 6 Encryption c Xin He (University at Buffalo) CSE 431/531 Algorithm Analysis and Design 1 / 29

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Algorithm Analysis It means: estimating the recourses required. c Xin He (University at Buffalo) CSE 431/531 Algorithm Analysis and Design 2 / 29
Algorithm Analysis It means: estimating the recourses required. The resources of algorithms: time and space . c Xin He (University at Buffalo) CSE 431/531 Algorithm Analysis and Design 2 / 29

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Algorithm Analysis It means: estimating the recourses required. The resources of algorithms: time and space . We mainly consider time: harder to estimate; often more critical. c Xin He (University at Buffalo) CSE 431/531 Algorithm Analysis and Design 2 / 29
Algorithm Analysis It means: estimating the recourses required. The resources of algorithms: time and space . We mainly consider time: harder to estimate; often more critical. The efficiency of an algorithm is measured by a runtime function T ( n ) . c Xin He (University at Buffalo) CSE 431/531 Algorithm Analysis and Design 2 / 29

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Algorithm Analysis It means: estimating the recourses required. The resources of algorithms: time and space . We mainly consider time: harder to estimate; often more critical. The efficiency of an algorithm is measured by a runtime function T ( n ) . n is the size of the input. c Xin He (University at Buffalo) CSE 431/531 Algorithm Analysis and Design 2 / 29
Algorithm Analysis It means: estimating the recourses required. The resources of algorithms: time and space . We mainly consider time: harder to estimate; often more critical. The efficiency of an algorithm is measured by a runtime function T ( n ) . n is the size of the input. Strictly speaking, n is the # of bits needed to represent input. c Xin He (University at Buffalo) CSE 431/531 Algorithm Analysis and Design 2 / 29

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Algorithm Analysis It means: estimating the recourses required. The resources of algorithms: time and space . We mainly consider time: harder to estimate; often more critical. The efficiency of an algorithm is measured by a runtime function T ( n ) . n is the size of the input. Strictly speaking, n is the # of bits needed to represent input. Commonly, n is the # of items in the input, if each item is of fixed size . c Xin He (University at Buffalo) CSE 431/531 Algorithm Analysis and Design 2 / 29
Algorithm Analysis It means: estimating the recourses required. The resources of algorithms: time and space . We mainly consider time: harder to estimate; often more critical. The efficiency of an algorithm is measured by a runtime function T ( n ) . n is the size of the input. Strictly speaking, n is the # of bits needed to represent input. Commonly, n is the # of items in the input, if each item is of fixed size . This makes no difference in asymptotic analysis in most cases.

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note01 - Outline 1 Algorithm Analysis Growth rate functions...

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