�ݺ�ߣshows by User: rkumardmh

�ݺ�ߣshows by User: rkumardmh / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: rkumardmh / Mon, 15 Feb 2016 13:48:40 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: rkumardmh Quiz 2 /slideshow/quiz-2-58276198/58276198 quiz2-160215134840
Quz on analysis and design of algorithm]]>
Quz on analysis and design of algorithm]]> Mon, 15 Feb 2016 13:48:40 GMT /slideshow/quiz-2-58276198/58276198 rkumardmh@slideshare.net(rkumardmh) Quiz 2 rkumardmh Quz on analysis and design of algorithm <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/quiz2-160215134840-thumbnail.jpg?width=120&height=120&fit=bounds" /> Quz on analysis and design of algorithm

Quiz 2 from Rajesh K Shukla

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0 Quiz 1 /slideshow/quiz-1-58276136/58276136 quiz1-160215134727
Quiz on analysis of algorithm]]>
Quiz on analysis of algorithm]]> Mon, 15 Feb 2016 13:47:27 GMT /slideshow/quiz-1-58276136/58276136 rkumardmh@slideshare.net(rkumardmh) Quiz 1 rkumardmh Quiz on analysis of algorithm <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/quiz1-160215134727-thumbnail.jpg?width=120&height=120&fit=bounds" /> Quiz on analysis of algorithm

Quiz 1 from Rajesh K Shukla

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0 Pop operation /slideshow/pop-operation/58276080 pop-operation-160215134550
Stack operation: Push and POP]]>
Stack operation: Push and POP]]> Mon, 15 Feb 2016 13:45:50 GMT /slideshow/pop-operation/58276080 rkumardmh@slideshare.net(rkumardmh) Pop operation rkumardmh Stack operation: Push and POP <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pop-operation-160215134550-thumbnail.jpg?width=120&height=120&fit=bounds" /> Stack operation: Push and POP

Pop operation from Rajesh K Shukla

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0 Stack: Recursion and Iteration /rkumardmh/stack-recursion-and-iteration push-160215133936
Comparison of Recursion and Iteration]]>
Comparison of Recursion and Iteration]]> Mon, 15 Feb 2016 13:39:36 GMT /rkumardmh/stack-recursion-and-iteration rkumardmh@slideshare.net(rkumardmh) Stack: Recursion and Iteration rkumardmh Comparison of Recursion and Iteration <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/push-160215133936-thumbnail.jpg?width=120&height=120&fit=bounds" /> Comparison of Recursion and Iteration

Stack: Recursion and Iteration from Rajesh K Shukla

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0 Little o and little omega /slideshow/little-o-and-little-omega/58274858 little-o-and-little-omega-160215131359
The little Oh (o) notation is a method of expressing the an upper bound on the growth rate of an algorithm’s running time which may or may not be asymptotically tight therefore little oh(o) is also called a loose upper bound we use little oh (o) notations to denote upper bound that is asymptotically not tight.]]>
The little Oh (o) notation is a method of expressing the an upper bound on the growth rate of an algorithm’s running time which may or may not be asymptotically tight therefore little oh(o) is also called a loose upper bound we use little oh (o) notations to denote upper bound that is asymptotically not tight.]]> Mon, 15 Feb 2016 13:13:59 GMT /slideshow/little-o-and-little-omega/58274858 rkumardmh@slideshare.net(rkumardmh) Little o and little omega rkumardmh The little Oh (o) notation is a method of expressing the an upper bound on the growth rate of an algorithm’s running time which may or may not be asymptotically tight therefore little oh(o) is also called a loose upper bound we use little oh (o) notations to denote upper bound that is asymptotically not tight. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/little-o-and-little-omega-160215131359-thumbnail.jpg?width=120&height=120&fit=bounds" /> The little Oh (o) notation is a method of expressing the an upper bound on the growth rate of an algorithm’s running time which may or may not be asymptotically tight therefore little oh(o) is also called a loose upper bound we use little oh (o) notations to denote upper bound that is asymptotically not tight.

Little o and little omega from Rajesh K Shukla

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0 Theta notation /slideshow/theta-notation/58274816 theta-notation-160215131248
The Theta (Θ) notation is a method of expressing the asymptotic tight bound on the growth rate of an algorithm’s running time both from above and below ends i.e. upper bound and lower bound.]]>
The Theta (Θ) notation is a method of expressing the asymptotic tight bound on the growth rate of an algorithm’s running time both from above and below ends i.e. upper bound and lower bound.]]> Mon, 15 Feb 2016 13:12:48 GMT /slideshow/theta-notation/58274816 rkumardmh@slideshare.net(rkumardmh) Theta notation rkumardmh The Theta (Θ) notation is a method of expressing the asymptotic tight bound on the growth rate of an algorithm’s running time both from above and below ends i.e. upper bound and lower bound. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/theta-notation-160215131248-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Theta (Θ) notation is a method of expressing the asymptotic tight bound on the growth rate of an algorithm’s running time both from above and below ends i.e. upper bound and lower bound.

Theta notation from Rajesh K Shukla

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0 The bog oh notation /slideshow/the-bog-oh-notation/58274778 the-bog-oh-notation-160215131152
The Big Oh (O) is the most commonly used notation to express an algorism’s performance. The big Oh (O) notation is a method of expressing the upper bound on the growth rate of an algorithm’s running time. In other words we can say that it is the longest amount of time, an algorithm could possibly take to finish it therefore the “big-Oh” or O-Notation is used for worst-case analysis of the algorithm.]]>
The Big Oh (O) is the most commonly used notation to express an algorism’s performance. The big Oh (O) notation is a method of expressing the upper bound on the growth rate of an algorithm’s running time. In other words we can say that it is the longest amount of time, an algorithm could possibly take to finish it therefore the “big-Oh” or O-Notation is used for worst-case analysis of the algorithm.]]> Mon, 15 Feb 2016 13:11:52 GMT /slideshow/the-bog-oh-notation/58274778 rkumardmh@slideshare.net(rkumardmh) The bog oh notation rkumardmh The Big Oh (O) is the most commonly used notation to express an algorism’s performance. The big Oh (O) notation is a method of expressing the upper bound on the growth rate of an algorithm’s running time. In other words we can say that it is the longest amount of time, an algorithm could possibly take to finish it therefore the “big-Oh” or O-Notation is used for worst-case analysis of the algorithm. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/the-bog-oh-notation-160215131152-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Big Oh (O) is the most commonly used notation to express an algorism’s performance. The big Oh (O) notation is a method of expressing the upper bound on the growth rate of an algorithm’s running time. In other words we can say that it is the longest amount of time, an algorithm could possibly take to finish it therefore the “big-Oh” or O-Notation is used for worst-case analysis of the algorithm.

The bog oh notation from Rajesh K Shukla

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0 Big omega /slideshow/big-omega/58274707 big-omega-160215130939
The Big Omega () notation is a method of expressing the lower bound on the growth rate of an algorithm’s running time. In other words we can say that it is the minimum amount of time, an algorithm could possibly take to finish it therefore the “big-Omega” or -Notation is used for best-case analysis of the algorithm.]]>
The Big Omega () notation is a method of expressing the lower bound on the growth rate of an algorithm’s running time. In other words we can say that it is the minimum amount of time, an algorithm could possibly take to finish it therefore the “big-Omega” or -Notation is used for best-case analysis of the algorithm.]]> Mon, 15 Feb 2016 13:09:39 GMT /slideshow/big-omega/58274707 rkumardmh@slideshare.net(rkumardmh) Big omega rkumardmh The Big Omega () notation is a method of expressing the lower bound on the growth rate of an algorithm’s running time. In other words we can say that it is the minimum amount of time, an algorithm could possibly take to finish it therefore the “big-Omega” or -Notation is used for best-case analysis of the algorithm. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/big-omega-160215130939-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Big Omega () notation is a method of expressing the lower bound on the growth rate of an algorithm’s running time. In other words we can say that it is the minimum amount of time, an algorithm could possibly take to finish it therefore the “big-Omega” or -Notation is used for best-case analysis of the algorithm.

Big omega from Rajesh K Shukla

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0 Lecture Note-1: Algorithm and Its Properties /slideshow/lecture-note1-algorithm-and-its-properties/58274447 lecture-1-160215130310
An algorithm is a tool for solving any computational problem. It may be defined as a sequence of finite, precise and unambiguous instructions which are applied either to perform a computation or to solve a computational problem. These instructions are applied on some raw data called the input, and the solution of the problem produced is called the output.]]>
An algorithm is a tool for solving any computational problem. It may be defined as a sequence of finite, precise and unambiguous instructions which are applied either to perform a computation or to solve a computational problem. These instructions are applied on some raw data called the input, and the solution of the problem produced is called the output.]]> Mon, 15 Feb 2016 13:03:10 GMT /slideshow/lecture-note1-algorithm-and-its-properties/58274447 rkumardmh@slideshare.net(rkumardmh) Lecture Note-1: Algorithm and Its Properties rkumardmh An algorithm is a tool for solving any computational problem. It may be defined as a sequence of finite, precise and unambiguous instructions which are applied either to perform a computation or to solve a computational problem. These instructions are applied on some raw data called the input, and the solution of the problem produced is called the output. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lecture-1-160215130310-thumbnail.jpg?width=120&height=120&fit=bounds" /> An algorithm is a tool for solving any computational problem. It may be defined as a sequence of finite, precise and unambiguous instructions which are applied either to perform a computation or to solve a computational problem. These instructions are applied on some raw data called the input, and the solution of the problem produced is called the output.

Lecture Note-1: Algorithm and Its Properties from Rajesh K Shukla

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0 Lecture Note-2: Performance analysis of Algorithms /slideshow/lecture-note2-performance-analysis-of-algorithms/58274227 lecture-1-160215125650
The execution of an algorithm requires various resources of the computer system to complete the task. The performance of algorithms depends on the use of these resources. The important resources which contribute to the efficiency of the algorithms are the memory space and the time required for successful execution of algorithm. The efficiency of an algorithm is measured in terms of the time and the space required for its execution therefore analysis of algorithms is divided into two categories]]>
The execution of an algorithm requires various resources of the computer system to complete the task. The performance of algorithms depends on the use of these resources. The important resources which contribute to the efficiency of the algorithms are the memory space and the time required for successful execution of algorithm. The efficiency of an algorithm is measured in terms of the time and the space required for its execution therefore analysis of algorithms is divided into two categories]]> Mon, 15 Feb 2016 12:56:50 GMT /slideshow/lecture-note2-performance-analysis-of-algorithms/58274227 rkumardmh@slideshare.net(rkumardmh) Lecture Note-2: Performance analysis of Algorithms rkumardmh The execution of an algorithm requires various resources of the computer system to complete the task. The performance of algorithms depends on the use of these resources. The important resources which contribute to the efficiency of the algorithms are the memory space and the time required for successful execution of algorithm. The efficiency of an algorithm is measured in terms of the time and the space required for its execution therefore analysis of algorithms is divided into two categories <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lecture-1-160215125650-thumbnail.jpg?width=120&height=120&fit=bounds" /> The execution of an algorithm requires various resources of the computer system to complete the task. The performance of algorithms depends on the use of these resources. The important resources which contribute to the efficiency of the algorithms are the memory space and the time required for successful execution of algorithm. The efficiency of an algorithm is measured in terms of the time and the space required for its execution therefore analysis of algorithms is divided into two categories

Lecture Note-2: Performance analysis of Algorithms from Rajesh K Shukla

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0 https://cdn.slidesharecdn.com/profile-photo-rkumardmh-48x48.jpg?cb=1586786961 Academician, Researcher and Author Interests:Literature,Soft classical Music https://cdn.slidesharecdn.com/ss_thumbnails/quiz2-160215134840-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/quiz-2-58276198/58276198 Quiz 2 https://cdn.slidesharecdn.com/ss_thumbnails/quiz1-160215134727-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/quiz-1-58276136/58276136 Quiz 1 https://cdn.slidesharecdn.com/ss_thumbnails/pop-operation-160215134550-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/pop-operation/58276080 Pop operation