The document discusses different types of parsing including:
1) Top-down parsing which starts at the root node and builds the parse tree recursively, requiring backtracking for ambiguous grammars.
2) Bottom-up parsing which starts at the leaf nodes and applies grammar rules in reverse to reach the start symbol using shift-reduce parsing.
3) LL(1) and LR parsing which are predictive parsing techniques using parsing tables constructed from FIRST and FOLLOW sets to avoid backtracking.
The document discusses the all pairs shortest path problem, which aims to find the shortest distance between every pair of vertices in a graph. It explains that the algorithm works by calculating the minimum cost to traverse between nodes using intermediate nodes, according to the equation A_k(i,j)=min{A_{k-1}(i,j), A_{k-1}(i,k), A_{k-1}(k,j)}. An example is provided to illustrate calculating the shortest path between nodes over multiple iterations of the algorithm.
This document describes shift-reduce parsing. Shift-reduce parsing is a bottom-up parsing approach where the input string is collapsed by reducing parts of the string according to production rules until the start symbol is reached, as opposed to top-down parsing which expands symbols. It uses two main data structures: an input buffer and a stack. Initially, it puts the input string in the buffer and a start symbol on the stack. It then performs the basic operations of shift, which moves symbols from the buffer to the stack, and reduce, which replaces symbols on the stack according to production rules. It halts when the start symbol remains on the stack and the buffer is empty, indicating successful parsing.
Dijkstra's algorithm finds the shortest paths from a source node to all other nodes in a graph. It works by maintaining two sets - one for nodes whose shortest paths are determined, and one for remaining nodes. It iteratively selects the node with the smallest distance from the source, calculates the distances to its neighbors, and moves them to the determined set until all nodes are processed. Some applications include finding fastest routes in transportation networks like road maps or flight schedules.
The document describes pushdown automata (PDA). A PDA has a tape, stack, finite control, and transition function. It accepts or rejects strings by reading symbols on the tape, pushing/popping symbols on the stack, and changing state according to the transition function. The transition function defines the possible moves of the PDA based on the current state, tape symbol, and stack symbol. If the PDA halts in a final state with an empty stack, the string is accepted. PDAs can recognize any context-free language. Examples are given of PDAs for specific languages.
TSP is np- hard problem which has number of solution but it's difficult to find optimal solution . I gave here fast,easy and efficient solution on TSP using one algorithm with good explanation.Hope you understood very well.
This document discusses hashing and different techniques for implementing dictionaries using hashing. It begins by explaining that dictionaries store elements using keys to allow for quick lookups. It then discusses different data structures that can be used, focusing on hash tables. The document explains that hashing allows for constant-time lookups on average by using a hash function to map keys to table positions. It discusses collision resolution techniques like chaining, linear probing, and double hashing to handle collisions when the hash function maps multiple keys to the same position.
The document discusses different types of parsing including:
1) Top-down parsing which starts at the root node and builds the parse tree recursively, requiring backtracking for ambiguous grammars.
2) Bottom-up parsing which starts at the leaf nodes and applies grammar rules in reverse to reach the start symbol using shift-reduce parsing.
3) LL(1) and LR parsing which are predictive parsing techniques using parsing tables constructed from FIRST and FOLLOW sets to avoid backtracking.
The document discusses the all pairs shortest path problem, which aims to find the shortest distance between every pair of vertices in a graph. It explains that the algorithm works by calculating the minimum cost to traverse between nodes using intermediate nodes, according to the equation A_k(i,j)=min{A_{k-1}(i,j), A_{k-1}(i,k), A_{k-1}(k,j)}. An example is provided to illustrate calculating the shortest path between nodes over multiple iterations of the algorithm.
This document describes shift-reduce parsing. Shift-reduce parsing is a bottom-up parsing approach where the input string is collapsed by reducing parts of the string according to production rules until the start symbol is reached, as opposed to top-down parsing which expands symbols. It uses two main data structures: an input buffer and a stack. Initially, it puts the input string in the buffer and a start symbol on the stack. It then performs the basic operations of shift, which moves symbols from the buffer to the stack, and reduce, which replaces symbols on the stack according to production rules. It halts when the start symbol remains on the stack and the buffer is empty, indicating successful parsing.
Dijkstra's algorithm finds the shortest paths from a source node to all other nodes in a graph. It works by maintaining two sets - one for nodes whose shortest paths are determined, and one for remaining nodes. It iteratively selects the node with the smallest distance from the source, calculates the distances to its neighbors, and moves them to the determined set until all nodes are processed. Some applications include finding fastest routes in transportation networks like road maps or flight schedules.
The document describes pushdown automata (PDA). A PDA has a tape, stack, finite control, and transition function. It accepts or rejects strings by reading symbols on the tape, pushing/popping symbols on the stack, and changing state according to the transition function. The transition function defines the possible moves of the PDA based on the current state, tape symbol, and stack symbol. If the PDA halts in a final state with an empty stack, the string is accepted. PDAs can recognize any context-free language. Examples are given of PDAs for specific languages.
TSP is np- hard problem which has number of solution but it's difficult to find optimal solution . I gave here fast,easy and efficient solution on TSP using one algorithm with good explanation.Hope you understood very well.
This document discusses hashing and different techniques for implementing dictionaries using hashing. It begins by explaining that dictionaries store elements using keys to allow for quick lookups. It then discusses different data structures that can be used, focusing on hash tables. The document explains that hashing allows for constant-time lookups on average by using a hash function to map keys to table positions. It discusses collision resolution techniques like chaining, linear probing, and double hashing to handle collisions when the hash function maps multiple keys to the same position.
This document discusses strongly connected components (SCCs) in directed graphs. It defines an SCC as a maximal set of vertices where each vertex is mutually reachable from every other. The algorithm works by running DFS on the original graph and its transpose to find SCCs, processing vertices in decreasing finishing time order from the first DFS. Vertices belonging to the same DFS tree in the second graph traversal are in the same SCC.
Sorting arranges data in a specific order by comparing elements according to a key value. The main sorting methods are bubble sort, selection sort, insertion sort, quicksort, mergesort, heapsort, and radix sort. Hashing maps data to table indexes using a hash function to provide direct access, with the potential for collisions. Common hash functions include division, mid-square, and folding methods.
This document discusses minimum spanning trees. It defines a minimum spanning tree as a spanning tree of a connected, undirected graph that has a minimum total cost among all spanning trees of that graph. The document provides properties of minimum spanning trees, including that they are acyclic, connect all vertices, and have n-1 edges for a graph with n vertices. Applications of minimum spanning trees mentioned include communication networks, power grids, and laying telephone wires to minimize total length.
This document discusses different graph traversal algorithms: depth-first traversal, breadth-first traversal, and their implementations. Depth-first traversal uses a stack and can output nodes in either preorder or postorder. Breadth-first traversal uses a queue and outputs nodes level-by-level. Pseudocode and examples are provided for both algorithms. Review questions ask the reader to trace the output order of different traversals on sample graphs.
This document discusses predicates and quantifiers in predicate logic. It begins by explaining the limitations of propositional logic in expressing statements involving variables and relationships between objects. It then introduces predicates as statements involving variables, and quantifiers like universal ("for all") and existential ("there exists") to express the extent to which a predicate is true. Examples are provided to demonstrate how predicates and quantifiers can be used to represent statements and enable logical reasoning. The document also covers translating statements between natural language and predicate logic, and negating quantified statements.
This document introduces information theory and channel capacity models. It discusses several channel models including the binary symmetric channel (BSC), binary erasure channel, and additive white Gaussian noise channel. It explains how channel capacity is defined as the maximum rate of error-free transmission and derives the capacity for some basic channels. The document also covers channel coding techniques like interleaving that can improve performance by converting burst errors into random errors.
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
This document discusses parsing with context-free grammars. It begins by introducing context-free grammars and their use in parsing sentences. It then discusses parsing as a search problem, and presents top-down and bottom-up parsing algorithms. Top-down parsing builds trees from the root node down, while bottom-up parsing builds trees from the leaves up. Both approaches have advantages and disadvantages related to efficiency. The document also introduces probabilistic context-free grammars, which augment grammars with rule probabilities, and discusses how these can be used for disambiguation.
This document discusses optimal binary search trees and provides an example problem. It begins with basic definitions of binary search trees and optimal binary search trees. It then shows an example problem with keys 1, 2, 3 and calculates the cost as 17. The document explains how to use dynamic programming to find the optimal binary search tree for keys 10, 12, 16, 21 with frequencies 4, 2, 6, 3. It provides the solution matrix and explains that the minimum cost is 2 with the optimal tree as 10, 12, 16, 21.
The document discusses the divide-and-conquer algorithm design paradigm. It explains that a problem is divided into smaller subproblems, the subproblems are solved independently, and then the solutions are combined. Recurrence equations can be used to analyze the running time of divide-and-conquer algorithms. The document provides examples of solving recurrences using methods like the recursion tree method and the master theorem.
This document discusses hashing and hash tables. It begins by introducing hash tables and describing how hashing works by mapping keys to array indices using a hash function to allow for fast insertion, deletion and search operations in O(1) average time. However, hash tables do not support ordering of elements efficiently. The document then discusses issues with hash functions such as collisions when different keys map to the same index. It describes techniques for collision resolution including separate chaining, where each index points to a linked list, and open addressing techniques like linear probing, quadratic probing and double hashing that resolve collisions by probing alternate indices in the array.
The document discusses shortest path problems and algorithms. It defines the shortest path problem as finding the minimum weight path between two vertices in a weighted graph. It presents the Bellman-Ford algorithm, which can handle graphs with negative edge weights but detects negative cycles. It also presents Dijkstra's algorithm, which only works for graphs without negative edge weights. Key steps of the algorithms include initialization, relaxation of edges to update distance estimates, and ensuring the shortest path property is satisfied.
Divide and conquer is a general algorithm design paradigm where a problem is divided into subproblems, the subproblems are solved independently, and the results are combined to solve the original problem. Binary search is a divide and conquer algorithm that searches for a target value in a sorted array by repeatedly dividing the search interval in half. It compares the target to the middle element of the array, and then searches either the upper or lower half depending on whether the target is greater or less than the middle element. Finding the maximum and minimum elements in an array can also be solved using divide and conquer by recursively finding the max and min of halves of the array and combining the results.
Public key cryptography uses key pairs - a public key and a private key - to encrypt and decrypt messages. The public key can be shared widely, while the private key is kept secret. This allows users to securely share encrypted messages without having to first share secret keys. Common applications of public key cryptography include public key encryption and digital signatures.
Bottom-up parsing builds a derivation by working from the input sentence back toward the start symbol S. It is preferred in practice and also called LR parsing, where L means tokens are read left to right and R means it constructs a rightmost derivation. The two main types are operator-precedence parsing and LR parsing, which covers a wide range of grammars through techniques like SLR, LALR, and LR parsing. LR parsing reduces a string to the start symbol by inverting productions through identifying handles and replacing them.
This document discusses bottom-up parsing and LR parsing. Bottom-up parsing starts from the leaf nodes of a parse tree and works upward to the root node by applying grammar rules in reverse. LR parsing is a type of bottom-up parsing that uses shift-reduce parsing with two steps: shifting input symbols onto a stack, and reducing grammar rules on the stack. The document describes LR parsers, types of LR parsers like SLR(1) and LALR(1), and the LR parsing algorithm. It also compares bottom-up LR parsing to top-down LL parsing.
This document discusses trees and their applications in three sentences:
Trees are connected graphs without cycles that can be used to model hierarchical data. Common tree types include binary search trees for storing and retrieving data efficiently and decision trees for modeling sequential decision processes. Tree traversal algorithms like preorder, inorder and postorder specify ways to systematically visit all vertices in a rooted tree.
This document discusses the merge sort algorithm for sorting a sequence of numbers. It begins by introducing the divide and conquer approach, which merge sort uses. It then provides an example of how merge sort works, dividing the sequence into halves, sorting the halves recursively, and then merging the sorted halves together. The document proceeds to provide pseudocode for the merge sort and merge algorithms. It analyzes the running time of merge sort using recursion trees, determining that it runs in O(n log n) time. Finally, it covers techniques for solving recurrence relations that arise in algorithms like divide and conquer approaches.
The document discusses properties and theorems related to trees in graph theory. Some key points include:
- A tree is a connected acyclic graph with n vertices that has n-1 edges.
- There is a one-to-one correspondence between labeled trees with n vertices and sequences of n-2 labels, as proven by Cayley's theorem.
- Every connected graph has at least one spanning tree, which is a subgraph that contains all vertices. Fundamental circuits are formed when a chord is added to a spanning tree.
- Cyclic interchange can be used to generate all possible spanning trees by adding and removing edges.
The document discusses hashing techniques for storing and retrieving data from memory. It covers hash functions, hash tables, open addressing techniques like linear probing and quadratic probing, and closed hashing using separate chaining. Hashing maps keys to memory addresses using a hash function to store and find data independently of the number of items. Collisions may occur and different collision resolution methods are used like open addressing that resolves collisions by probing in the table or closed hashing that uses separate chaining with linked lists. The efficiency of hashing depends on factors like load factor and average number of probes.
This document discusses computer algorithms and provides examples of algorithms in Python. It begins by defining an algorithm and providing examples of sorting algorithms like insertion sort, selection sort, and merge sort. It then discusses searching algorithms like linear search and binary search, including their time complexities. Other topics covered include advantages of Python, types of problems solved by algorithms, and limitations of binary search.
This document discusses strongly connected components (SCCs) in directed graphs. It defines an SCC as a maximal set of vertices where each vertex is mutually reachable from every other. The algorithm works by running DFS on the original graph and its transpose to find SCCs, processing vertices in decreasing finishing time order from the first DFS. Vertices belonging to the same DFS tree in the second graph traversal are in the same SCC.
Sorting arranges data in a specific order by comparing elements according to a key value. The main sorting methods are bubble sort, selection sort, insertion sort, quicksort, mergesort, heapsort, and radix sort. Hashing maps data to table indexes using a hash function to provide direct access, with the potential for collisions. Common hash functions include division, mid-square, and folding methods.
This document discusses minimum spanning trees. It defines a minimum spanning tree as a spanning tree of a connected, undirected graph that has a minimum total cost among all spanning trees of that graph. The document provides properties of minimum spanning trees, including that they are acyclic, connect all vertices, and have n-1 edges for a graph with n vertices. Applications of minimum spanning trees mentioned include communication networks, power grids, and laying telephone wires to minimize total length.
This document discusses different graph traversal algorithms: depth-first traversal, breadth-first traversal, and their implementations. Depth-first traversal uses a stack and can output nodes in either preorder or postorder. Breadth-first traversal uses a queue and outputs nodes level-by-level. Pseudocode and examples are provided for both algorithms. Review questions ask the reader to trace the output order of different traversals on sample graphs.
This document discusses predicates and quantifiers in predicate logic. It begins by explaining the limitations of propositional logic in expressing statements involving variables and relationships between objects. It then introduces predicates as statements involving variables, and quantifiers like universal ("for all") and existential ("there exists") to express the extent to which a predicate is true. Examples are provided to demonstrate how predicates and quantifiers can be used to represent statements and enable logical reasoning. The document also covers translating statements between natural language and predicate logic, and negating quantified statements.
This document introduces information theory and channel capacity models. It discusses several channel models including the binary symmetric channel (BSC), binary erasure channel, and additive white Gaussian noise channel. It explains how channel capacity is defined as the maximum rate of error-free transmission and derives the capacity for some basic channels. The document also covers channel coding techniques like interleaving that can improve performance by converting burst errors into random errors.
The document discusses the theory of NP-completeness. It begins by defining the complexity classes P, NP, NP-hard, and NP-complete. It then explains the concepts of reduction and how none of the NP-complete problems can be solved in polynomial time deterministically. The document provides examples of NP-complete problems like satisfiability (SAT), vertex cover, and the traveling salesman problem. It shows how nondeterministic algorithms can solve these problems and how they can be transformed into SAT instances. Finally, it proves that SAT is the first NP-complete problem by showing it is in NP and NP-hard.
This document discusses parsing with context-free grammars. It begins by introducing context-free grammars and their use in parsing sentences. It then discusses parsing as a search problem, and presents top-down and bottom-up parsing algorithms. Top-down parsing builds trees from the root node down, while bottom-up parsing builds trees from the leaves up. Both approaches have advantages and disadvantages related to efficiency. The document also introduces probabilistic context-free grammars, which augment grammars with rule probabilities, and discusses how these can be used for disambiguation.
This document discusses optimal binary search trees and provides an example problem. It begins with basic definitions of binary search trees and optimal binary search trees. It then shows an example problem with keys 1, 2, 3 and calculates the cost as 17. The document explains how to use dynamic programming to find the optimal binary search tree for keys 10, 12, 16, 21 with frequencies 4, 2, 6, 3. It provides the solution matrix and explains that the minimum cost is 2 with the optimal tree as 10, 12, 16, 21.
The document discusses the divide-and-conquer algorithm design paradigm. It explains that a problem is divided into smaller subproblems, the subproblems are solved independently, and then the solutions are combined. Recurrence equations can be used to analyze the running time of divide-and-conquer algorithms. The document provides examples of solving recurrences using methods like the recursion tree method and the master theorem.
This document discusses hashing and hash tables. It begins by introducing hash tables and describing how hashing works by mapping keys to array indices using a hash function to allow for fast insertion, deletion and search operations in O(1) average time. However, hash tables do not support ordering of elements efficiently. The document then discusses issues with hash functions such as collisions when different keys map to the same index. It describes techniques for collision resolution including separate chaining, where each index points to a linked list, and open addressing techniques like linear probing, quadratic probing and double hashing that resolve collisions by probing alternate indices in the array.
The document discusses shortest path problems and algorithms. It defines the shortest path problem as finding the minimum weight path between two vertices in a weighted graph. It presents the Bellman-Ford algorithm, which can handle graphs with negative edge weights but detects negative cycles. It also presents Dijkstra's algorithm, which only works for graphs without negative edge weights. Key steps of the algorithms include initialization, relaxation of edges to update distance estimates, and ensuring the shortest path property is satisfied.
Divide and conquer is a general algorithm design paradigm where a problem is divided into subproblems, the subproblems are solved independently, and the results are combined to solve the original problem. Binary search is a divide and conquer algorithm that searches for a target value in a sorted array by repeatedly dividing the search interval in half. It compares the target to the middle element of the array, and then searches either the upper or lower half depending on whether the target is greater or less than the middle element. Finding the maximum and minimum elements in an array can also be solved using divide and conquer by recursively finding the max and min of halves of the array and combining the results.
Public key cryptography uses key pairs - a public key and a private key - to encrypt and decrypt messages. The public key can be shared widely, while the private key is kept secret. This allows users to securely share encrypted messages without having to first share secret keys. Common applications of public key cryptography include public key encryption and digital signatures.
Bottom-up parsing builds a derivation by working from the input sentence back toward the start symbol S. It is preferred in practice and also called LR parsing, where L means tokens are read left to right and R means it constructs a rightmost derivation. The two main types are operator-precedence parsing and LR parsing, which covers a wide range of grammars through techniques like SLR, LALR, and LR parsing. LR parsing reduces a string to the start symbol by inverting productions through identifying handles and replacing them.
This document discusses bottom-up parsing and LR parsing. Bottom-up parsing starts from the leaf nodes of a parse tree and works upward to the root node by applying grammar rules in reverse. LR parsing is a type of bottom-up parsing that uses shift-reduce parsing with two steps: shifting input symbols onto a stack, and reducing grammar rules on the stack. The document describes LR parsers, types of LR parsers like SLR(1) and LALR(1), and the LR parsing algorithm. It also compares bottom-up LR parsing to top-down LL parsing.
This document discusses trees and their applications in three sentences:
Trees are connected graphs without cycles that can be used to model hierarchical data. Common tree types include binary search trees for storing and retrieving data efficiently and decision trees for modeling sequential decision processes. Tree traversal algorithms like preorder, inorder and postorder specify ways to systematically visit all vertices in a rooted tree.
This document discusses the merge sort algorithm for sorting a sequence of numbers. It begins by introducing the divide and conquer approach, which merge sort uses. It then provides an example of how merge sort works, dividing the sequence into halves, sorting the halves recursively, and then merging the sorted halves together. The document proceeds to provide pseudocode for the merge sort and merge algorithms. It analyzes the running time of merge sort using recursion trees, determining that it runs in O(n log n) time. Finally, it covers techniques for solving recurrence relations that arise in algorithms like divide and conquer approaches.
The document discusses properties and theorems related to trees in graph theory. Some key points include:
- A tree is a connected acyclic graph with n vertices that has n-1 edges.
- There is a one-to-one correspondence between labeled trees with n vertices and sequences of n-2 labels, as proven by Cayley's theorem.
- Every connected graph has at least one spanning tree, which is a subgraph that contains all vertices. Fundamental circuits are formed when a chord is added to a spanning tree.
- Cyclic interchange can be used to generate all possible spanning trees by adding and removing edges.
The document discusses hashing techniques for storing and retrieving data from memory. It covers hash functions, hash tables, open addressing techniques like linear probing and quadratic probing, and closed hashing using separate chaining. Hashing maps keys to memory addresses using a hash function to store and find data independently of the number of items. Collisions may occur and different collision resolution methods are used like open addressing that resolves collisions by probing in the table or closed hashing that uses separate chaining with linked lists. The efficiency of hashing depends on factors like load factor and average number of probes.
This document discusses computer algorithms and provides examples of algorithms in Python. It begins by defining an algorithm and providing examples of sorting algorithms like insertion sort, selection sort, and merge sort. It then discusses searching algorithms like linear search and binary search, including their time complexities. Other topics covered include advantages of Python, types of problems solved by algorithms, and limitations of binary search.
This document discusses algorithms and sorting. It begins by defining an algorithm as a precise set of instructions for solving a problem or completing a task. Algorithms can be expressed as flowcharts or pseudocode. Sorting is introduced as arranging elements in an array in a relevant order like ascending or descending. Selection sort is provided as an example sorting algorithm. It works by iterating through the array, finding the minimum value, and swapping it into the sorted position. The example shows selection sort sorting an array of numbers in ascending order over three iterations.
In our thesis work, we try to find out the efficiency of several sorting algorithms and generate a comparative report according to performance, based on experimental data size and data order for all algorithm. To do this we have researched, analyzed about 9 different types of well-known sorting algorithms. We convert the algorithms to programmable code, compile and run with different set of data. We keep the sorting algorithm’s description as it is. We give focus on, how the algorithms work, considering their total operation count (assignment count, comparison count) and complexity based on our same data set for all algorithm. We write programming code for each sorting algorithm in C programming language. In our investigation, we have also worked with big and small data for different cases (ordered, pre-ordered, random, disordered) and put their result in different tables. We show the increasing ratio to compare the result. we also show the data in graphical chart for view comparative report in same window. We mark their efficiency with point and ranked them. At last we discussed their result of efficiency in a single table. We modify the merge sort and try to make an improved tri-merge sorting algorithm that is more efficient than marge sort. Theoretically if we divide and conquer with higher number its result is better, some paper exists on it, but to manage the algorithm, there cost lot of operations count. Like, if we consider quadratic divide-conquer, its manage complexly is huge than binary divide-conquer that why we generally use binary merge. We found trimerge is theoretically and practically true based on investigation data set. Tri-marge take some more compare operation for manage and sort when data remain 1 or 2 at last stage, whereas binary merge don’t need such compare. But for big data size tri-merge gain lot of operation count that give significant result that declare tri-merge is more efficient than merge sort algorithm. We also experiment with penta-merge algorithm which give more better result but algorithm and implementation is too complex.
We shall try to define the tri-merge algorithm so that it can be used to implement in any
programming language. It will help students, researchers to use the algorithm, as like we
got the various algorithm structure over the internet.
This document provides an overview and introduction to the concepts taught in a data structures and algorithms course. It discusses the goals of reinforcing that every data structure has costs and benefits, learning commonly used data structures, and understanding how to analyze the efficiency of algorithms. Key topics covered include abstract data types, common data structures, algorithm analysis techniques like best/worst/average cases and asymptotic notation, and examples of analyzing the time complexity of various algorithms. The document emphasizes that problems can have multiple potential algorithms and that problems should be carefully defined in terms of inputs, outputs, and resource constraints.
1. Variables - Learn to conveniently store data in your Python programs!
2. Numbers - Learn how numbers work behind the scenes in your Python programs!
3. Strings - Master the written word and automate messages using text!
4. Logic and Data Structures - Teach your Python programs to think and decide!
5. Loops - Save time and effort, by making computers do the hard work for you!
6. Functions - Automate Tasks by Creating your very own Python Functions that you can use over and over!
7. OOP - Add Python to Your Resumé By Mastering Object-Oriented Programming, an industry-standard programming technique!
The document discusses order statistics and medians. It defines order statistics as the ith smallest element in a data set and notes that the median is the middle element when the data set size is odd or the average of the two middle elements when the size is even. It then describes algorithms for finding the minimum, maximum, second smallest element, and any order statistic in expected linear time using a randomized selection algorithm. Finally, it provides an overview of generic programming in C++ using templates for functions and classes.
1. Counting sort and radix sort can sort in linear time O(n) by exploiting properties of the input rather than just comparisons. Counting sort assumes integers as input and radix sort assumes digitized numbers.
2. Bucket sort also runs in linear time if inputs are uniformly distributed between 0 and 1. It divides the range into buckets and distributes inputs into the corresponding buckets which are then sorted.
3. The comparison-based lower bound of Ω(nlogn) does not apply to these algorithms because they do not rely solely on comparisons. Counting sort counts occurrences rather than comparing, and radix/bucket sort distribute into buckets based on digits/positions rather than comparisons.
data structures using C 2 sem BCA univeristy of mysoreambikavenkatesh2
The document discusses reallocating memory using the realloc() function in C. It provides code to allocate memory for an integer array, print the memory addresses, reallocate the array to a larger size, and print the new memory addresses. The memory addresses for the previously allocated blocks do not change after reallocating, but new contiguous blocks are added to increase the array size.
This document discusses data structures and algorithms. It provides course objectives which include imparting concepts of data structures and algorithms, introducing searching and sorting techniques, and developing applications using suitable data structures. Course outcomes include understanding algorithm performance analysis, concepts of data structures, linear data structures, and identifying efficient data structure implementations. The document also covers algorithm basics, specifications, expressions, analysis techniques and asymptotic notations for analyzing algorithms.
The document discusses various sorting algorithms including exchange sorts like bubble sort and quicksort, selection sorts like straight selection sort, and tree sorts like heap sort. For each algorithm, it provides an overview of the approach, pseudocode, analysis of time complexity, and examples. Key algorithms covered are bubble sort (O(n2)), quicksort (average O(n log n)), selection sort (O(n2)), and heap sort (O(n log n)).
Linear search examines each element of a list sequentially, one by one, and checks if it is the target value. It has a time complexity of O(n) as it requires searching through each element in the worst case. While simple to implement, linear search is inefficient for large lists as other algorithms like binary search require fewer comparisons.
The document discusses limitations of algorithms and methods for establishing lower bounds on algorithmic complexity. It covers four main topics: [1] efficiency classes and lower bounds, [2] decision trees for deriving lower bounds, [3] adversary arguments and problem reduction techniques for lower bounds, and [4] classifications of problem complexity including P, NP, NP-complete, and exponential time problems.
The document provides an overview of data structures and algorithms. It discusses key topics like:
1) Different types of data structures including primitive, linear, non-linear, and arrays.
2) The importance of algorithms and how to write them using steps, comments, variables, and control structures.
3) Common operations on data structures like insertion, deletion, searching, and sorting.
4) Different looping and selection statements that can be used in algorithms like for, while, and if-then-else.
5) How arrays can be used as a structure to store multiple values in a contiguous block of memory.
ds 1 Introduction to Data Structures.pptAlliVinay1
This document provides an introduction and overview of data structures. It begins by defining key terms like data, information, and entities. It then discusses how data structures represent logical relationships between data elements and how they should be easy to process and represent relationships. The document classifies common data structures as linear, non-linear, homogeneous, non-homogeneous, dynamic, and static. It also provides examples of basic notations, algorithms, control structures, and applications of different data structure types like arrays, stacks, queues, linked lists, trees, and graphs. Finally, it discusses complexity analysis and the tradeoff between time and space.
This document provides an overview of data structures and algorithms. It discusses pseudo code, abstract data types, atomic and composite data, data structures, algorithm efficiency using Big O notation, and various searching algorithms like sequential, binary, and hashed list searches. Key concepts covered include pseudo code structure and syntax, defining algorithms with headers and conditions, and analyzing different search algorithms.
The document provides an introduction to various data structures and algorithms concepts. It discusses different types of data structures like simple, compound, linear and non-linear data structures. It also covers algorithm analysis concepts like time complexity, asymptotic notations and different searching and sorting algorithms like linear search, binary search, bubble sort, selection sort, insertion sort, quick sort and merge sort. It provides pseudocode examples of recursive algorithms like factorial, Fibonacci sequence and towers of Hanoi problem.
jn;lm;lkm';m';;lmppt of data structure.pdfVinayNassa3
This document provides an introduction to data structures, algorithms, and complexity analysis. It begins with an overview of data structures and their classification as simple, compound, linear, or non-linear. Common operations on data structures like adding, deleting, and searching elements are described. The document then covers algorithm analysis, asymptotic notation, and examples of recursive and sorting algorithms like bubble sort, selection sort, and their time complexities. Searching techniques like linear, binary, and Fibonacci search are also summarized.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
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This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
2. Problem
A professor has an unsorted text file containing student “netid”s and scores.
The professor wants the data to be ordered both alphabetically and by score.
Additionally, the professor wants to know the top scorer(s) and the mean and median
score.
3. Goal of program
● Read a file containing students’ grades
● Sort grades by username and score (and output)
● Find top scoring student(s) (and output)
● Compute mean and median (and output)
5. Process
Read user input Read and parse file
Sort data Calculations
Output file
Read input
Processing
6. Process
Read user input Read and parse file
Sort data Calculations
Output file
Read input
Processing
7. Read User input
● Command line arguments
○ Consists of an array of C-strings (argv) and a number (argc)
○ argc – Number of strings
○ argv – C-strings contain each command line argument
○ Note: argv[0] contains the name of the program
● fopen() for reading from file
○ Takes a file path and returns a file pointer
○ Returns NULL if file does not exist
○ “r” specifies read operation
8. Grades structure
● Using structs, we can create a grade
● More on structs here
○ Access members with ‘.’
○ grade1.score returns the user’s score
● Basically a holder containing:
○ int score - integer containing student’s score
○ char* netid - C-string containing netid
■ Character pointer
■ Holds the address of the first character in the string
9. Process
Read user input Read and parse file
Sort data Calculations
Output file
Read input
Processing
10. Read and parse data loop
while(not end of file)
Get line of input
Parse line of input
11. Add initial loop
● Initially don’t know number of entries
● Can’t allocate correct space
● To fix, iterate twice
○ Loop 1: Count entries
○ Allocate space using malloc()
○ Loop 2: Parse entries
12. Read and parse data (revised)
while(not end of file)
Get line of input
Parse line of input
while(not end of file)
Increment count
Allocate
13. Read and parse data (revised)
while(not end of file)
Get line of input
Parse line of input
while(not end of file)
Increment count
Allocate
14. Counting lines of file
● We will use getLine() to read file
● This returns -1 when end of file is reached
● Increment counter until -1 is returned
15. Read and parse data (revised)
while(not end of file)
Get line of input
Parse line of input
while(not end of file)
Increment count
Allocate
16. Memory allocation
● Use malloc() to reserve space for array of grades
● Discussed under advanced topics here
● Does 2 things
○ Reserves desired amount of memory
○ Returns pointer to that memory
17. Read and parse data (revised)
while(not end of file)
Get line of input
Parse line of input
while(not end of file)
Increment count
Allocate
18. Read line of input
● Call getLine() (described here)
○ Pass file pointer, character pointer (for string), and size pointer
○ Updates passed character pointer to read string
○ Updates size pointer to size of string (disregarded)
19. Read and parse data (revised)
while(not end of file)
Get line of input
Parse line of input
while(not end of file)
Increment count
Allocate
20. Parsing
● Parse string
● First part: netid
○ Use strsep() to separate the string based on the comma
○ Described here
○ Assign to grades[i].netid a character pointer to the string (returned by strsep())
○ For our use, the pointer must be set to NULL before getting the line
● Second part: score
○ Use atoi() (described here) to parse an integer from a string
○ Assign to grades[i].score the score of element of the array grades
22. Process
Read user input Read and parse file
Sort data Calculations
Output file
Read input
Processing
23. Sort data
● We will use a selection sort (described here)
○ Search through the unsorted part of array
○ Find minimum element and swap with first element
■ Using pointers to swap is described here
○ Decrease size of unsorted part of array
● To sort netids, we will use strcmp() to identify smallest element
○ If the return value of strcmp() is negative, first parameter is smaller
○ If the return value of strcmp() is positive, second parameter is smaller
○ strcmp() is described here
● To sort scores, we will use ‘<’ to identify smallest element
25. Process
Read user input Read and parse file
Sort data Calculations
Output file
Read input
Processing
26. Calculations
● Median
○ Use sorted-by-score array
○ If number of scores is odd, use middle element
○ If number of scores is even, use average of middle 2 elements
● Mean
○ Add up total of scores in array and divide by number of elements
● Top scorer(s)
○ Find highest score (end of sorted array)
○ Iterate backwards until score is less than highest
○ From this index to the end are the top scorers
28. Process
Read user input Read and parse file
Sort data Calculations
Output file
Read input
Processing
29. Output file
● At each step, the output file is being created
○ After each sort and calculations print
○ fprintf() is described here
○ Is like printf to a file rather than the console
● File pointer is closed at termination of program
○ fclose() writes the file