The document summarizes a lecture on data structures including lists, stacks, queues and deques. It discusses:
1) Lists and tuples can be implemented using arrays in Python. Lists are mutable while tuples are immutable.
2) Stacks follow LIFO (last-in, first-out) order and can be implemented using arrays. The push and pop operations run in O(1) time while doubling the array size on overflow takes amortized O(1) time.
3) Stacks have applications in undo/redo functions, method call tracking, and matching parentheses in expressions.
The document discusses data structures and introduces common data structures like stacks and queues. It explains that stacks follow LIFO order and have push and pop operations, while queues follow FIFO order and have enqueue and dequeue operations. Array-based and linked list implementations of stacks and queues are described, with their time and space complexities analyzed. Exceptions for invalid operations like popping an empty stack are also covered.
this is an presentation about Big O Notation. Big O notaion is very useful to check the limitation and effeciecy of an algorithm in its worst cases.in these slides the examples about O(1),O(n),O(n^2) and O(n!) with some example algorithms in C++.
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
The document discusses solutions to several algorithm questions.
For Q1, it summarizes that incrementing and resetting a binary counter can be done in O(n) time by keeping a pointer to the highest set bit.
For Q2, it shows that a queue can be implemented with two stacks in O(1) amortized time by pushing and popping between the stacks as needed.
For Q3, it explains that while the amortized costs of insert and extract-min for a binary heap are O(logn) and O(1) respectively, this does not imply the overall time complexity of heapsort is O(n).
For Q4, it proposes a solution
This document provides an overview of stacks as an abstract data type (ADT). It defines a stack as a last-in first-out data structure for storing arbitrary objects. The key stack operations of push, pop, top, and size are described. Exceptions that can occur for empty stacks are discussed. An array-based implementation of stacks is presented, including algorithms for the stack operations and analysis of its performance and limitations. Applications of stacks like undo buffers and method call stacks are mentioned. Finally, an example of using a stack to check matching parentheses in an expression is provided.
This document summarizes a lecture on the Standard Template Library (STL) given by R Anirudh. The lecture covered key topics in STL including containers like vectors, pairs, strings, sets, maps, stacks and queues. It explained how STL can be used to efficiently implement algorithms and solve problems related to sorting, searching and graphs. Examples were provided for common STL functions and various online judge problems were listed for practice. Resources for further reading on STL were also shared.
The document summarizes several linear sorting algorithms, including bucket sort, counting sort, general bucket sort, and radix sort. Counting sort runs in O(n+k) time and O(k) space, where k is the range of integer keys, and is stable. Radix sort uses a stable sorting algorithm like counting sort to sort based on each digit of d-digit numbers, resulting in O(d(n+k)) time for sorting n numbers with d digits in the range [1,k].
This document provides an introduction to data structures and algorithms. It begins by discussing how to create programs through requirements analysis, design, coding, testing, and debugging. It then defines data structures as how data is organized and operated on, and explains that learning data structures and algorithms is important for performing tasks efficiently. Examples are provided to illustrate storing student data in an array and representing polynomials using arrays or linked lists. The document also covers searching arrays, recursive functions and binary search, abstract data types, performance analysis in terms of time and space complexity, and asymptotic notations for analyzing algorithms.
The document discusses data structures and introduces common data structures like stacks and queues. It explains that stacks follow LIFO order and have push and pop operations, while queues follow FIFO order and have enqueue and dequeue operations. Array-based and linked list implementations of stacks and queues are described, with their time and space complexities analyzed. Exceptions for invalid operations like popping an empty stack are also covered.
this is an presentation about Big O Notation. Big O notaion is very useful to check the limitation and effeciecy of an algorithm in its worst cases.in these slides the examples about O(1),O(n),O(n^2) and O(n!) with some example algorithms in C++.
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
The document discusses solutions to several algorithm questions.
For Q1, it summarizes that incrementing and resetting a binary counter can be done in O(n) time by keeping a pointer to the highest set bit.
For Q2, it shows that a queue can be implemented with two stacks in O(1) amortized time by pushing and popping between the stacks as needed.
For Q3, it explains that while the amortized costs of insert and extract-min for a binary heap are O(logn) and O(1) respectively, this does not imply the overall time complexity of heapsort is O(n).
For Q4, it proposes a solution
This document provides an overview of stacks as an abstract data type (ADT). It defines a stack as a last-in first-out data structure for storing arbitrary objects. The key stack operations of push, pop, top, and size are described. Exceptions that can occur for empty stacks are discussed. An array-based implementation of stacks is presented, including algorithms for the stack operations and analysis of its performance and limitations. Applications of stacks like undo buffers and method call stacks are mentioned. Finally, an example of using a stack to check matching parentheses in an expression is provided.
This document summarizes a lecture on the Standard Template Library (STL) given by R Anirudh. The lecture covered key topics in STL including containers like vectors, pairs, strings, sets, maps, stacks and queues. It explained how STL can be used to efficiently implement algorithms and solve problems related to sorting, searching and graphs. Examples were provided for common STL functions and various online judge problems were listed for practice. Resources for further reading on STL were also shared.
The document summarizes several linear sorting algorithms, including bucket sort, counting sort, general bucket sort, and radix sort. Counting sort runs in O(n+k) time and O(k) space, where k is the range of integer keys, and is stable. Radix sort uses a stable sorting algorithm like counting sort to sort based on each digit of d-digit numbers, resulting in O(d(n+k)) time for sorting n numbers with d digits in the range [1,k].
This document provides an introduction to data structures and algorithms. It begins by discussing how to create programs through requirements analysis, design, coding, testing, and debugging. It then defines data structures as how data is organized and operated on, and explains that learning data structures and algorithms is important for performing tasks efficiently. Examples are provided to illustrate storing student data in an array and representing polynomials using arrays or linked lists. The document also covers searching arrays, recursive functions and binary search, abstract data types, performance analysis in terms of time and space complexity, and asymptotic notations for analyzing algorithms.
The document discusses data structures and algorithms. It begins by introducing data structures and how they organize data to enable efficient operations. It provides examples of using arrays and linked lists to represent polynomials. The document then covers searching algorithms like binary search and recursive functions. It discusses abstract data types and how they separate an object's specification from its implementation. Finally, it covers analyzing algorithms, including analyzing their time and space complexity using asymptotic notations.
Arrays are a simple data structure for storing a group of elements of the same type in contiguous memory locations. Elements in an array are identified by an index, with one-dimensional arrays having a single index and two-dimensional arrays having two indices for row and column. Common operations on arrays include traversing elements, searching for a value, and sorting the elements. Arrays can be declared statically at compile-time or dynamically allocated at runtime using pointers and memory allocation functions.
The document discusses stacks and queues as fundamental data structures. It covers stack and queue operations like push, pop, enqueue, and dequeue. It presents implementations of stacks and queues using both arrays and linked lists. It discusses dynamic resizing of arrays to allow for efficient pushing and popping. The document also covers using generics to create parameterized data structures like Stack<Item> that avoid casting. Several applications of stacks are described, including evaluating arithmetic expressions using a two-stack algorithm and modeling function calls in a compiler using a run-time stack.
The document discusses algorithms complexity and data structures efficiency, explaining that algorithm complexity can be measured using asymptotic notation like O(n) or O(n^2) to represent operations scaling linearly or quadratically with input size, and different data structures have varying time efficiency for operations like add, find, and delete.
This document discusses stacks and queues as data structures. It begins by defining a stack as a linear collection where elements are added and removed from the top in a last-in, first-out (LIFO) manner. Common stack operations like push, pop, and peek are described. It then discusses applications of stacks like undo sequences and method calls. The document also defines queues as collections where elements are added to the rear and removed from the front in a first-in, first-out (FIFO) manner. Common queue operations and applications like waiting lists and printer access are also covered. Finally, it discusses implementations of stacks and queues using arrays and how to handle overflow and underflow cases.
A priori and a posteriori analysis are two methods for analyzing algorithms. A priori analysis involves determining the time and space complexity of an algorithm without running it on a specific system, while a posteriori analysis involves analyzing an algorithm after running it on a system. Big-O notation is commonly used to describe an algorithm's time complexity as the input size increases. Common time complexities include constant, logarithmic, linear, quadratic, and exponential time.
An algorithm is a finite set of instructions to accomplish a predefined task. Performance of an algorithm is measured by its time and space complexity, with common metrics being big O, big Omega, and big Theta notation. Common data structures include arrays, linked lists, stacks, queues, trees and graphs. Key concepts are asymptotic analysis of algorithms, recursion, and analyzing complexity classes like constant, linear, quadratic and logarithmic time.
This document discusses algorithms and their analysis. It begins by defining an algorithm and its key characteristics like being finite, definite, and terminating after a finite number of steps. It then discusses designing algorithms to minimize cost and analyzing algorithms to predict their performance. Various algorithm design techniques are covered like divide and conquer, binary search, and its recursive implementation. Asymptotic notations like Big-O, Omega, and Theta are introduced to analyze time and space complexity. Specific algorithms like merge sort, quicksort, and their recursive implementations are explained in detail.
This document outlines the syllabus for a course on data structures and algorithms using Java. It covers topics such as the role of algorithms and data structures, algorithm design techniques, types of data structures including primitive types, arrays, stacks, queues, linked lists, trees, graphs, and algorithm analysis. Specific algorithms and data structures discussed include sorting, searching, priority queues, stacks, queues, linked lists, trees, graphs, hashing, and complexity theory.
1. Hash tables are good for random access of elements but not sequential access. When records need to be accessed sequentially, hashing can be problematic because elements are stored in random locations instead of consecutively.
2. To find the successor of a node in a binary search tree, we take the right child. This operation has a runtime complexity of O(1).
3. When comparing operations like insertion, deletion, and searching between different data structures, arrays generally have the best performance for insertion and searching, while linked lists have better performance for deletion and allow for easy insertion/deletion anywhere. Binary search trees fall between these two.
This document discusses various data structures and their implementations in C++ using templates. It begins by reviewing basic linear data structures like lists, stacks, and queues. It then covers implementing these structures using array-based and linked representations in C++. Specifically, it describes how to implement lists, stacks, and queues using templates and both array and linked representations. It also compares the performance of array vs linked implementations.
Homework Assignment – Array Technical DocumentWrite a technical .pdfaroraopticals15
Homework Assignment – Array Technical Document
Write a technical document that describes the structure and use of arrays. The document should
be 3 to 5 pages and include an Introduction section, giving a brief synopsis of the document and
arrays, a Body section, describing arrays and giving an annotated example of their use as a
programming construct, and a conclusion to revisit important information about arrays described
in the Body of the document. Some suggested material to include:
Declaring arrays of various types
Array pointers
Printing and processing arrays
Sorting and searching arrays
Multidimensional arrays
Indexing arrays of various dimension
Array representation in memory by data type
Passing arrays as arguments
If you find any useful images on the Internet, you can use them as long as you cite the source in
end notes.
Solution
Array is a collection of variables of the same type that are referenced by a common name.
Specific elements or variables in the array are accessed by means of index into the array.
If taking about C, In C all arrays consist of contiguous memory locations. The lowest address
corresponds to the first element in the array while the largest address corresponds to the last
element in the array.
C supports both single and multi-dimensional arrays.
1) Single Dimension Arrays:-
Syntax:- type var_name[size];
where type is the type of each element in the array, var_name is any valid identifier, and size is
the number of elements in the array which has to be a constant value.
*Array always use zero as index to first element.
The valid indices for array above are 0 .. 4, i.e. 0 .. number of elements - 1
For Example :- To load an array with values 0 .. 99
int x[100] ;
int i ;
for ( i = 0; i < 100; i++ )
x[i] = i ;
To determine to size of an array at run time the sizeof operator is used. This returns the size in
bytes of its argument. The name of the array is given as the operand
size_of_array = sizeof ( array_name ) ;
2) Initialisg array:-
Arrays can be initialised at time of declaration in the following manner.
type array[ size ] = { value list };
For Example :-
int i[5] = {1, 2, 3, 4, 5 } ;
i[0] = 1, i[1] = 2, etc.
The size specification in the declaration may be omitted which causes the compiler to count the
number of elements in the value list and allocate appropriate storage.
For Example :- int i[ ] = { 1, 2, 3, 4, 5 } ;
3) Multidimensional array:-
Multidimensional arrays of any dimension are possible in C but in practice only two or three
dimensional arrays are workable. The most common multidimensional array is a two
dimensional array for example the computer display, board games, a mathematical matrix etc.
Syntax :type name [ rows ] [ columns ] ;
For Example :- 2D array of dimension 2 X 3.
int d[ 2 ] [ 3 ] ;
A two dimensional array is actually an array of arrays, in the above case an array of two integer
arrays (the rows) each with three elements, and is stored row-wise in memory.
For Example :- Program to fill .
The document discusses data structures and algorithms. It defines key concepts like primitive data types, data structures, static vs dynamic structures, abstract data types, algorithm design, analysis of time and space complexity, recursion, stacks and common stack operations like push and pop. Examples are provided to illustrate factorial calculation using recursion and implementation of a stack.
The document discusses data structures and algorithms. It defines key concepts like primitive data types, data structures, static vs dynamic structures, abstract data types, algorithm design, analysis of time and space complexity, and recursion. It provides examples of algorithms and data structures like stacks and using recursion to calculate factorials. The document covers fundamental topics in data structures and algorithms.
The document discusses data structures and algorithms. It defines key concepts like primitive data types, data structures, static vs dynamic structures, abstract data types, algorithm design, analysis of time and space complexity, and recursion. It provides examples of algorithms and data structures like arrays, stacks and the factorial function to illustrate recursive and iterative implementations. Problem solving techniques like defining the problem, designing algorithms, analyzing and testing solutions are also covered.
The document discusses data structures and algorithms. It defines key concepts like primitive data types, data structures, static vs dynamic structures, abstract data types, algorithm analysis including time and space complexity, and common algorithm design techniques like recursion. It provides examples of algorithms and data structures like stacks and using recursion to calculate factorials. The document covers fundamental topics in data structures and algorithms.
This document discusses data structures and algorithms. It begins by defining data structures as the logical organization of data and primitive data types like integers that hold single pieces of data. It then discusses static versus dynamic data structures and abstract data types. The document outlines the main steps in problem solving as defining the problem, designing algorithms, analyzing algorithms, implementing, testing, and maintaining solutions. It provides examples of space and time complexity analysis and discusses analyzing recursive algorithms through repeated substitution and telescoping methods.
The document discusses data structures and algorithms. It defines key concepts like primitive data types, data structures, static vs dynamic structures, abstract data types, algorithm design, analysis of time and space complexity, and recursion. It provides examples of algorithms and data structures like stacks and using recursion to calculate factorials. The document covers fundamental topics in data structures and algorithms.
Data structure and algorithm using javaNarayan Sau
This presentation created for people who like to go back to basics of data structure and its implementation. This presentation mostly helps B.Tech , Bsc Computer science students as well as all programmer who wants to develop software in core areas.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
More Related Content
Similar to Lecture 4 - List, Stack, Queues, Deques.pdf
The document discusses data structures and algorithms. It begins by introducing data structures and how they organize data to enable efficient operations. It provides examples of using arrays and linked lists to represent polynomials. The document then covers searching algorithms like binary search and recursive functions. It discusses abstract data types and how they separate an object's specification from its implementation. Finally, it covers analyzing algorithms, including analyzing their time and space complexity using asymptotic notations.
Arrays are a simple data structure for storing a group of elements of the same type in contiguous memory locations. Elements in an array are identified by an index, with one-dimensional arrays having a single index and two-dimensional arrays having two indices for row and column. Common operations on arrays include traversing elements, searching for a value, and sorting the elements. Arrays can be declared statically at compile-time or dynamically allocated at runtime using pointers and memory allocation functions.
The document discusses stacks and queues as fundamental data structures. It covers stack and queue operations like push, pop, enqueue, and dequeue. It presents implementations of stacks and queues using both arrays and linked lists. It discusses dynamic resizing of arrays to allow for efficient pushing and popping. The document also covers using generics to create parameterized data structures like Stack<Item> that avoid casting. Several applications of stacks are described, including evaluating arithmetic expressions using a two-stack algorithm and modeling function calls in a compiler using a run-time stack.
The document discusses algorithms complexity and data structures efficiency, explaining that algorithm complexity can be measured using asymptotic notation like O(n) or O(n^2) to represent operations scaling linearly or quadratically with input size, and different data structures have varying time efficiency for operations like add, find, and delete.
This document discusses stacks and queues as data structures. It begins by defining a stack as a linear collection where elements are added and removed from the top in a last-in, first-out (LIFO) manner. Common stack operations like push, pop, and peek are described. It then discusses applications of stacks like undo sequences and method calls. The document also defines queues as collections where elements are added to the rear and removed from the front in a first-in, first-out (FIFO) manner. Common queue operations and applications like waiting lists and printer access are also covered. Finally, it discusses implementations of stacks and queues using arrays and how to handle overflow and underflow cases.
A priori and a posteriori analysis are two methods for analyzing algorithms. A priori analysis involves determining the time and space complexity of an algorithm without running it on a specific system, while a posteriori analysis involves analyzing an algorithm after running it on a system. Big-O notation is commonly used to describe an algorithm's time complexity as the input size increases. Common time complexities include constant, logarithmic, linear, quadratic, and exponential time.
An algorithm is a finite set of instructions to accomplish a predefined task. Performance of an algorithm is measured by its time and space complexity, with common metrics being big O, big Omega, and big Theta notation. Common data structures include arrays, linked lists, stacks, queues, trees and graphs. Key concepts are asymptotic analysis of algorithms, recursion, and analyzing complexity classes like constant, linear, quadratic and logarithmic time.
This document discusses algorithms and their analysis. It begins by defining an algorithm and its key characteristics like being finite, definite, and terminating after a finite number of steps. It then discusses designing algorithms to minimize cost and analyzing algorithms to predict their performance. Various algorithm design techniques are covered like divide and conquer, binary search, and its recursive implementation. Asymptotic notations like Big-O, Omega, and Theta are introduced to analyze time and space complexity. Specific algorithms like merge sort, quicksort, and their recursive implementations are explained in detail.
This document outlines the syllabus for a course on data structures and algorithms using Java. It covers topics such as the role of algorithms and data structures, algorithm design techniques, types of data structures including primitive types, arrays, stacks, queues, linked lists, trees, graphs, and algorithm analysis. Specific algorithms and data structures discussed include sorting, searching, priority queues, stacks, queues, linked lists, trees, graphs, hashing, and complexity theory.
1. Hash tables are good for random access of elements but not sequential access. When records need to be accessed sequentially, hashing can be problematic because elements are stored in random locations instead of consecutively.
2. To find the successor of a node in a binary search tree, we take the right child. This operation has a runtime complexity of O(1).
3. When comparing operations like insertion, deletion, and searching between different data structures, arrays generally have the best performance for insertion and searching, while linked lists have better performance for deletion and allow for easy insertion/deletion anywhere. Binary search trees fall between these two.
This document discusses various data structures and their implementations in C++ using templates. It begins by reviewing basic linear data structures like lists, stacks, and queues. It then covers implementing these structures using array-based and linked representations in C++. Specifically, it describes how to implement lists, stacks, and queues using templates and both array and linked representations. It also compares the performance of array vs linked implementations.
Homework Assignment – Array Technical DocumentWrite a technical .pdfaroraopticals15
Homework Assignment – Array Technical Document
Write a technical document that describes the structure and use of arrays. The document should
be 3 to 5 pages and include an Introduction section, giving a brief synopsis of the document and
arrays, a Body section, describing arrays and giving an annotated example of their use as a
programming construct, and a conclusion to revisit important information about arrays described
in the Body of the document. Some suggested material to include:
Declaring arrays of various types
Array pointers
Printing and processing arrays
Sorting and searching arrays
Multidimensional arrays
Indexing arrays of various dimension
Array representation in memory by data type
Passing arrays as arguments
If you find any useful images on the Internet, you can use them as long as you cite the source in
end notes.
Solution
Array is a collection of variables of the same type that are referenced by a common name.
Specific elements or variables in the array are accessed by means of index into the array.
If taking about C, In C all arrays consist of contiguous memory locations. The lowest address
corresponds to the first element in the array while the largest address corresponds to the last
element in the array.
C supports both single and multi-dimensional arrays.
1) Single Dimension Arrays:-
Syntax:- type var_name[size];
where type is the type of each element in the array, var_name is any valid identifier, and size is
the number of elements in the array which has to be a constant value.
*Array always use zero as index to first element.
The valid indices for array above are 0 .. 4, i.e. 0 .. number of elements - 1
For Example :- To load an array with values 0 .. 99
int x[100] ;
int i ;
for ( i = 0; i < 100; i++ )
x[i] = i ;
To determine to size of an array at run time the sizeof operator is used. This returns the size in
bytes of its argument. The name of the array is given as the operand
size_of_array = sizeof ( array_name ) ;
2) Initialisg array:-
Arrays can be initialised at time of declaration in the following manner.
type array[ size ] = { value list };
For Example :-
int i[5] = {1, 2, 3, 4, 5 } ;
i[0] = 1, i[1] = 2, etc.
The size specification in the declaration may be omitted which causes the compiler to count the
number of elements in the value list and allocate appropriate storage.
For Example :- int i[ ] = { 1, 2, 3, 4, 5 } ;
3) Multidimensional array:-
Multidimensional arrays of any dimension are possible in C but in practice only two or three
dimensional arrays are workable. The most common multidimensional array is a two
dimensional array for example the computer display, board games, a mathematical matrix etc.
Syntax :type name [ rows ] [ columns ] ;
For Example :- 2D array of dimension 2 X 3.
int d[ 2 ] [ 3 ] ;
A two dimensional array is actually an array of arrays, in the above case an array of two integer
arrays (the rows) each with three elements, and is stored row-wise in memory.
For Example :- Program to fill .
The document discusses data structures and algorithms. It defines key concepts like primitive data types, data structures, static vs dynamic structures, abstract data types, algorithm design, analysis of time and space complexity, recursion, stacks and common stack operations like push and pop. Examples are provided to illustrate factorial calculation using recursion and implementation of a stack.
The document discusses data structures and algorithms. It defines key concepts like primitive data types, data structures, static vs dynamic structures, abstract data types, algorithm design, analysis of time and space complexity, and recursion. It provides examples of algorithms and data structures like stacks and using recursion to calculate factorials. The document covers fundamental topics in data structures and algorithms.
The document discusses data structures and algorithms. It defines key concepts like primitive data types, data structures, static vs dynamic structures, abstract data types, algorithm design, analysis of time and space complexity, and recursion. It provides examples of algorithms and data structures like arrays, stacks and the factorial function to illustrate recursive and iterative implementations. Problem solving techniques like defining the problem, designing algorithms, analyzing and testing solutions are also covered.
The document discusses data structures and algorithms. It defines key concepts like primitive data types, data structures, static vs dynamic structures, abstract data types, algorithm analysis including time and space complexity, and common algorithm design techniques like recursion. It provides examples of algorithms and data structures like stacks and using recursion to calculate factorials. The document covers fundamental topics in data structures and algorithms.
This document discusses data structures and algorithms. It begins by defining data structures as the logical organization of data and primitive data types like integers that hold single pieces of data. It then discusses static versus dynamic data structures and abstract data types. The document outlines the main steps in problem solving as defining the problem, designing algorithms, analyzing algorithms, implementing, testing, and maintaining solutions. It provides examples of space and time complexity analysis and discusses analyzing recursive algorithms through repeated substitution and telescoping methods.
The document discusses data structures and algorithms. It defines key concepts like primitive data types, data structures, static vs dynamic structures, abstract data types, algorithm design, analysis of time and space complexity, and recursion. It provides examples of algorithms and data structures like stacks and using recursion to calculate factorials. The document covers fundamental topics in data structures and algorithms.
Data structure and algorithm using javaNarayan Sau
This presentation created for people who like to go back to basics of data structure and its implementation. This presentation mostly helps B.Tech , Bsc Computer science students as well as all programmer who wants to develop software in core areas.
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Cooperation Organisation and the Belt and Road Economic Initiative.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Lecture 4 - List, Stack, Queues, Deques.pdf
1. SE274 Data Structure
Lecture 4: List (array), Stacks, Queues, Deques
Mar 22, 2022
Instructor: Sunjun Kim
DGIST ICE
1
2. Exercise)
2
à O(n3)
à O(n2)
Under following assumptions:
* No individual sequence contains duplicate values.
(Assume A, B, and C contains N items each)
3. Before we start…
• Memory management of Array in Python
3
0100101010111011…
4. Python Sequence Classes
• Python has built-in types, list, tuple, and str.
• Each of these sequence types supports indexing to access an individual element
of a sequence, using a syntax such as A[i]
• Each of these types uses an array to represent the sequence.
• An array is a set of memory locations that can be addressed using consecutive indices, which,
in Python, start with index 0.
4
A
0 1 2 n
i
9. List and Tuple
List
• Defined as [A, B, C, … ]
• Mutable
Tuple
• Defined as (A, B, C, … )
• Immutable
9
The same: sequence data types (=stored as an array)
10. List and Tuple
List Tuple
10
*k: the index of the leftmost occurrence. k=n if there is no occurrence.
24. Stacks
24
The Stack ADT
• Main stack operations:
• push(object): inserts an
element
• object pop(): removes and
returns the last inserted
element
• Auxiliary stack operations:
• object top(): returns the last
inserted element without
removing it
• integer len(): returns the
number of elements stored
• boolean is_empty(): indicates
whether no elements are
stored
obj
push(obj)
obj
obj = pop()
obj
obj = top()
len()