This document discusses using the game show Letters and Numbers to illustrate computational and mathematical concepts. It describes implementing versions of the letters and numbers games in Excel/VBA and Delphi to generate all possible expression trees to solve problems. Student feedback on a related assignment was positive, finding the application of algorithms and programming to solve the games' problems interesting. The document concludes Letters and Numbers provides useful examples for showcasing fundamentals of computation and mathematics.
The document discusses binary search trees and their properties. It explains that a binary search tree is a binary tree where every node's left subtree contains values less than the node's value and the right subtree contains greater values. Operations like search, insert, delete can be done in O(h) time where h is the height of the tree. The height is O(log n) for balanced trees but can be O(n) for unbalanced trees. The document also provides examples of using a binary search tree to sort a set of numbers in O(n log n) time by building the BST and doing an inorder traversal.
The document discusses different number systems including binary, octal, decimal, and hexadecimal. It provides details on each system such as the base, digits used, applications, and how to convert between them. Binary uses only 0s and 1s and is the most fundamental system used in computing. Octal uses digits 0-7, with applications including older computer architectures. Decimal uses 0-9 and is the most common. Hexadecimal uses 0-9 and A-F, with each digit representing 4 bits, making it convenient for displaying colors and memory addresses.
This document discusses binary trees and tree traversal algorithms. It begins with definitions of trees and binary trees. It then covers tree traversal methods including preorder, inorder, postorder and level order traversal. Implementation of these traversals using recursion is shown. Applications to arithmetic expressions and propositional logic are discussed. Node structures for representing binary trees and evaluating expressions are also presented.
This document provides instruction on converting between binary and decimal number formats. It includes:
1. Lessons on binary math concepts like converting binary to decimal and vice versa using a bit value chart.
2. Examples of converting specific IP addresses between dotted decimal and binary formats by placing the binary digits in the chart and adding or subtracting column values.
3. Practice questions for readers to convert sample IP addresses between the two number formats on their own.
The document describes several Adobe interview test papers that the author took. It provides examples of questions asked in sections on coding (C/Java), data structures, algorithms, quantitative aptitude, and logical reasoning. Some example questions include finding the fourth smallest element in a binary search tree, reversing a linked list, checking if all computers are connected in a network, and problems involving arithmetic, triangles, and pie charts. The tests focused on fundamental concepts in coding, data structures, algorithms, and math.
This document discusses numerical methods in MATLAB, including root finding, interpolation, integration, and solving ordinary differential equations. It provides examples of using MATLAB functions like fzero, roots, interp1, quad, and ode45. The key functions and methods covered are:
- fzero finds roots of univariate functions numerically. roots finds roots of polynomials.
- interp1 performs one-dimensional interpolation using methods like nearest, linear, spline, and cubic interpolation.
- quad and quad8 numerically evaluate integrals of varying accuracy and order.
- ode23, ode45, ode113, ode15s, and ode23s solve non-stiff and stiff ordinary differential equations.
The document discusses finding the longest common subsequence between two sequences and provides an algorithm using dynamic programming. It explains using a matrix to store the current alignment results, where each cell Aij is calculated based on the adjacent cells, with scores considered. There are two steps - find the length of the LCS using the matrix, then trace back to find the exact alignment. It also discusses the knapsack problem and how dynamic programming can be applied to optimize combination problems.
The document discusses machine learning decision trees and the ID3 algorithm for constructing decision trees from training data. ID3 is a top-down, greedy search algorithm that uses information gain to select the attribute that best splits the training examples at each node, without backtracking. It recursively builds the tree by creating child nodes for each value of the selected attribute, then applies the same process to partition the examples at each child node.
The document discusses binary search trees and their properties. It explains that a binary search tree is a binary tree where every node's left subtree contains values less than the node's value and the right subtree contains greater values. Operations like search, insert, delete can be done in O(h) time where h is the height of the tree. The height is O(log n) for balanced trees but can be O(n) for unbalanced trees. The document also provides examples of using a binary search tree to sort a set of numbers in O(n log n) time by building the BST and doing an inorder traversal.
The document discusses different number systems including binary, octal, decimal, and hexadecimal. It provides details on each system such as the base, digits used, applications, and how to convert between them. Binary uses only 0s and 1s and is the most fundamental system used in computing. Octal uses digits 0-7, with applications including older computer architectures. Decimal uses 0-9 and is the most common. Hexadecimal uses 0-9 and A-F, with each digit representing 4 bits, making it convenient for displaying colors and memory addresses.
This document discusses binary trees and tree traversal algorithms. It begins with definitions of trees and binary trees. It then covers tree traversal methods including preorder, inorder, postorder and level order traversal. Implementation of these traversals using recursion is shown. Applications to arithmetic expressions and propositional logic are discussed. Node structures for representing binary trees and evaluating expressions are also presented.
This document provides instruction on converting between binary and decimal number formats. It includes:
1. Lessons on binary math concepts like converting binary to decimal and vice versa using a bit value chart.
2. Examples of converting specific IP addresses between dotted decimal and binary formats by placing the binary digits in the chart and adding or subtracting column values.
3. Practice questions for readers to convert sample IP addresses between the two number formats on their own.
The document describes several Adobe interview test papers that the author took. It provides examples of questions asked in sections on coding (C/Java), data structures, algorithms, quantitative aptitude, and logical reasoning. Some example questions include finding the fourth smallest element in a binary search tree, reversing a linked list, checking if all computers are connected in a network, and problems involving arithmetic, triangles, and pie charts. The tests focused on fundamental concepts in coding, data structures, algorithms, and math.
This document discusses numerical methods in MATLAB, including root finding, interpolation, integration, and solving ordinary differential equations. It provides examples of using MATLAB functions like fzero, roots, interp1, quad, and ode45. The key functions and methods covered are:
- fzero finds roots of univariate functions numerically. roots finds roots of polynomials.
- interp1 performs one-dimensional interpolation using methods like nearest, linear, spline, and cubic interpolation.
- quad and quad8 numerically evaluate integrals of varying accuracy and order.
- ode23, ode45, ode113, ode15s, and ode23s solve non-stiff and stiff ordinary differential equations.
The document discusses finding the longest common subsequence between two sequences and provides an algorithm using dynamic programming. It explains using a matrix to store the current alignment results, where each cell Aij is calculated based on the adjacent cells, with scores considered. There are two steps - find the length of the LCS using the matrix, then trace back to find the exact alignment. It also discusses the knapsack problem and how dynamic programming can be applied to optimize combination problems.
The document discusses machine learning decision trees and the ID3 algorithm for constructing decision trees from training data. ID3 is a top-down, greedy search algorithm that uses information gain to select the attribute that best splits the training examples at each node, without backtracking. It recursively builds the tree by creating child nodes for each value of the selected attribute, then applies the same process to partition the examples at each child node.
This document provides an overview of tree data structures and binary trees. It begins by defining trees and their basic concepts such as subtrees, leaves, levels, and roots. It then defines binary trees and contrasts them with general trees. The document discusses calculating the height of full binary trees and using trees to represent arithmetic expressions. It also covers traversing trees and different ways of representing trees in memory.
I am Elijah L. I am an Algorithm Assignment Expert at programminghomeworkhelp.com. I hold a Bachelor’s Degree in Programming, Leeds University, UK. I have been helping students with their homework for the past 6 years. I solve assignments related to Algorithms.
Visit programminghomeworkhelp.com or email support@programminghomeworkhelp.com.You can also call on +1 678 648 4277 for any assistance with Algorithm assignments.
Here are my slides for my preparation class for possible Master students in Electrical Engineering and Computer Science (Specialization in Computer Science)... for the entrance examination here at Cinvestav GDL.
17. Java data structures trees representation and traversalIntro C# Book
In this chapter we will discuss tree data structures, like trees and graphs. The abilities of these data structures are really important for the modern programming. Each of this data structures is used for building a model of real life problems, which are efficiently solved using this model.
The document discusses binary trees and binary search trees. It defines key terms like root, child, parent, leaves, height, depth. It explains tree traversal methods like preorder, inorder and postorder. It then describes binary search trees and how they store keys in a way that searching, insertion and deletion can be done efficiently in O(log n) time. It discusses implementation of operations like search, insert, delete on BSTs. It introduces balanced binary search trees like AVL trees that ensure height is O(log n) through rotations during insertions and deletions.
This document contains a pre-class worksheet for a class. It includes questions about analog vs digital measurements, number bases such as binary, octal, decimal and hexadecimal, and exercises to convert numbers between bases including binary to decimal conversions. Students are asked to fill in charts and show work for the conversions.
The document describes the Tangent math search engine which indexes math expressions using symbol pair tuples and uses these to retrieve relevant expressions for queries containing wildcards. It represents expressions as symbol layout trees and indexes pairs of symbols with their distances and positions to allow partial matching via wildcards. Queries are converted to tuples and matched against the formula index to retrieve candidate expressions based on symbol pair matches.
Trees. Defining, Creating and Traversing Trees. Traversing the File System
Binary Search Trees. Balanced Trees
Graphs and Graphs Traversal Algorithms
Exercises: Working with Trees and Graphs
This document provides an overview of trees and tree-like data structures. It discusses trees and related terminology like nodes, edges, and traversals. It also covers implementing trees recursively and with a Tree<T> class. Binary trees and different traversal orders are explained. Balanced search trees like AVL trees, red-black trees, and B-trees are introduced for maintaining search trees in a balanced way. Finally, graphs are defined as a set of nodes connected by edges.
Binary search trees (BSTs) are binary trees where all keys in the left subtree of a node are less than or equal to the key of that node, and all keys in the right subtree are greater than or equal to the node's key. Common BST operations include search, insertion, deletion, finding the minimum/maximum keys, and tree traversals like inorder, preorder, and postorder walks. These operations have worst-case time complexity proportional to the height of the tree.
The document provides an overview of different tree data structures including binary trees, binary search trees, AVL trees, B-trees, and B+ trees. It describes key properties such as balance factors for AVL trees and minimum/maximum node sizes for B-trees. Implementation details are given for binary trees, binary search trees, and some common tree operations like search, insert, delete. Applications of trees in indexing large datasets are also mentioned.
I am Samuel H. I am a Mechanical Engineering Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. Matlab, University of Alberta, Canada. I have been helping students with their homework for the past 12 years. I solve assignments related to Mechanical Engineering.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Mechanical Engineering Assignments.
This document provides information about different tree data structures including binary trees, binary search trees, AVL trees, red-black trees, splay trees, and B-trees. Binary search trees allow for fast searching and maintain an ordered structure. AVL and red-black trees are self-balancing binary search trees that ensure fast search, insert, and delete times by keeping the tree balanced. B-trees are multiway search trees that allow for efficient storage and retrieval of data in databases and file systems.
The document discusses functions in mathematics and programming. In mathematics, a function defines a relationship between inputs and outputs. The domain is the set of valid inputs, and the range is the set of valid outputs. In programming, functions perform actions and return values. The argument type specifies valid input types, analogous to the mathematical domain, while the return type specifies the output type, analogous to the range. The C standard library contains common mathematical functions like abs, sqrt, and cos. Functions can be used in expressions and assignments like variables.
The document discusses digital logic design and covers the following topics:
- Basics of logic gates and digital circuits including transistors, integration levels, and logic functions.
- Combinational circuits such as multiplexers, demultiplexers, decoders, comparators, adders, and arithmetic logic units (ALUs). Specific circuit examples and implementations are provided.
- Sequential circuits are mentioned but not covered in detail.
The document provides information about getting help with algorithm assignments. It lists a website, email address, and phone number that can be used for support regarding algorithm homework help.
The document contains exercises, hints, and solutions for analyzing algorithms from a textbook. It includes problems related to brute force algorithms, sorting algorithms like selection sort and bubble sort, and evaluating polynomials. The solutions analyze the time complexity of different algorithms, such as proving that a brute force polynomial evaluation algorithm is O(n^2) while a modified version is linear time. It also discusses whether sorting algorithms like selection sort and bubble sort preserve the original order of equal elements (i.e. whether they are stable).
In computer science, tree traversal (also known as tree search) is a form of graph traversal and refers to the process of visiting (checking and/or updating) each node in a tree data structure, exactly once. Such traversals are classified by the order in which the nodes are visited. The following algorithms are described for a binary tree, but they may be generalized to other trees as well.
The binary search is faster than the sequential search. The complexity of binary search is O(log n) whereas the complexity of a sequential search is O(n). Stacks are used to evaluate algebraic or arithmetic expressions using prefix or postfix notations. Heap sort involves creating a max heap from the array and then replacing the root with the last element and rebuilding the heap for the remaining elements, repeating this process to sort the entire array.
The document discusses tree data structures and binary search trees. It defines key tree terminology like root, internal and leaf nodes. It explains tree traversal algorithms like preorder, inorder and postorder traversal using recursion and iteration. It also describes operations on binary search trees like searching, inserting and deleting nodes. The complexity of these BST operations is provided. Algorithms to find the inorder predecessor and successor of a node in a BST are presented.
This document is the August 2006 monthly bulletin of statistics from Mongolia's National Statistical Office. It provides data on a variety of economic indicators including the state budget, monetary indicators, prices, external trade, balance of payments, industry, labor, agriculture, transport, household income and expenditure, health, and crime. The bulletin includes general notes on methodology and abbreviations used in the tables.
This document provides an overview of tree data structures and binary trees. It begins by defining trees and their basic concepts such as subtrees, leaves, levels, and roots. It then defines binary trees and contrasts them with general trees. The document discusses calculating the height of full binary trees and using trees to represent arithmetic expressions. It also covers traversing trees and different ways of representing trees in memory.
I am Elijah L. I am an Algorithm Assignment Expert at programminghomeworkhelp.com. I hold a Bachelor’s Degree in Programming, Leeds University, UK. I have been helping students with their homework for the past 6 years. I solve assignments related to Algorithms.
Visit programminghomeworkhelp.com or email support@programminghomeworkhelp.com.You can also call on +1 678 648 4277 for any assistance with Algorithm assignments.
Here are my slides for my preparation class for possible Master students in Electrical Engineering and Computer Science (Specialization in Computer Science)... for the entrance examination here at Cinvestav GDL.
17. Java data structures trees representation and traversalIntro C# Book
In this chapter we will discuss tree data structures, like trees and graphs. The abilities of these data structures are really important for the modern programming. Each of this data structures is used for building a model of real life problems, which are efficiently solved using this model.
The document discusses binary trees and binary search trees. It defines key terms like root, child, parent, leaves, height, depth. It explains tree traversal methods like preorder, inorder and postorder. It then describes binary search trees and how they store keys in a way that searching, insertion and deletion can be done efficiently in O(log n) time. It discusses implementation of operations like search, insert, delete on BSTs. It introduces balanced binary search trees like AVL trees that ensure height is O(log n) through rotations during insertions and deletions.
This document contains a pre-class worksheet for a class. It includes questions about analog vs digital measurements, number bases such as binary, octal, decimal and hexadecimal, and exercises to convert numbers between bases including binary to decimal conversions. Students are asked to fill in charts and show work for the conversions.
The document describes the Tangent math search engine which indexes math expressions using symbol pair tuples and uses these to retrieve relevant expressions for queries containing wildcards. It represents expressions as symbol layout trees and indexes pairs of symbols with their distances and positions to allow partial matching via wildcards. Queries are converted to tuples and matched against the formula index to retrieve candidate expressions based on symbol pair matches.
Trees. Defining, Creating and Traversing Trees. Traversing the File System
Binary Search Trees. Balanced Trees
Graphs and Graphs Traversal Algorithms
Exercises: Working with Trees and Graphs
This document provides an overview of trees and tree-like data structures. It discusses trees and related terminology like nodes, edges, and traversals. It also covers implementing trees recursively and with a Tree<T> class. Binary trees and different traversal orders are explained. Balanced search trees like AVL trees, red-black trees, and B-trees are introduced for maintaining search trees in a balanced way. Finally, graphs are defined as a set of nodes connected by edges.
Binary search trees (BSTs) are binary trees where all keys in the left subtree of a node are less than or equal to the key of that node, and all keys in the right subtree are greater than or equal to the node's key. Common BST operations include search, insertion, deletion, finding the minimum/maximum keys, and tree traversals like inorder, preorder, and postorder walks. These operations have worst-case time complexity proportional to the height of the tree.
The document provides an overview of different tree data structures including binary trees, binary search trees, AVL trees, B-trees, and B+ trees. It describes key properties such as balance factors for AVL trees and minimum/maximum node sizes for B-trees. Implementation details are given for binary trees, binary search trees, and some common tree operations like search, insert, delete. Applications of trees in indexing large datasets are also mentioned.
I am Samuel H. I am a Mechanical Engineering Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. Matlab, University of Alberta, Canada. I have been helping students with their homework for the past 12 years. I solve assignments related to Mechanical Engineering.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com.
You can also call on +1 678 648 4277 for any assistance with Mechanical Engineering Assignments.
This document provides information about different tree data structures including binary trees, binary search trees, AVL trees, red-black trees, splay trees, and B-trees. Binary search trees allow for fast searching and maintain an ordered structure. AVL and red-black trees are self-balancing binary search trees that ensure fast search, insert, and delete times by keeping the tree balanced. B-trees are multiway search trees that allow for efficient storage and retrieval of data in databases and file systems.
The document discusses functions in mathematics and programming. In mathematics, a function defines a relationship between inputs and outputs. The domain is the set of valid inputs, and the range is the set of valid outputs. In programming, functions perform actions and return values. The argument type specifies valid input types, analogous to the mathematical domain, while the return type specifies the output type, analogous to the range. The C standard library contains common mathematical functions like abs, sqrt, and cos. Functions can be used in expressions and assignments like variables.
The document discusses digital logic design and covers the following topics:
- Basics of logic gates and digital circuits including transistors, integration levels, and logic functions.
- Combinational circuits such as multiplexers, demultiplexers, decoders, comparators, adders, and arithmetic logic units (ALUs). Specific circuit examples and implementations are provided.
- Sequential circuits are mentioned but not covered in detail.
The document provides information about getting help with algorithm assignments. It lists a website, email address, and phone number that can be used for support regarding algorithm homework help.
The document contains exercises, hints, and solutions for analyzing algorithms from a textbook. It includes problems related to brute force algorithms, sorting algorithms like selection sort and bubble sort, and evaluating polynomials. The solutions analyze the time complexity of different algorithms, such as proving that a brute force polynomial evaluation algorithm is O(n^2) while a modified version is linear time. It also discusses whether sorting algorithms like selection sort and bubble sort preserve the original order of equal elements (i.e. whether they are stable).
In computer science, tree traversal (also known as tree search) is a form of graph traversal and refers to the process of visiting (checking and/or updating) each node in a tree data structure, exactly once. Such traversals are classified by the order in which the nodes are visited. The following algorithms are described for a binary tree, but they may be generalized to other trees as well.
The binary search is faster than the sequential search. The complexity of binary search is O(log n) whereas the complexity of a sequential search is O(n). Stacks are used to evaluate algebraic or arithmetic expressions using prefix or postfix notations. Heap sort involves creating a max heap from the array and then replacing the root with the last element and rebuilding the heap for the remaining elements, repeating this process to sort the entire array.
The document discusses tree data structures and binary search trees. It defines key tree terminology like root, internal and leaf nodes. It explains tree traversal algorithms like preorder, inorder and postorder traversal using recursion and iteration. It also describes operations on binary search trees like searching, inserting and deleting nodes. The complexity of these BST operations is provided. Algorithms to find the inorder predecessor and successor of a node in a BST are presented.
This document is the August 2006 monthly bulletin of statistics from Mongolia's National Statistical Office. It provides data on a variety of economic indicators including the state budget, monetary indicators, prices, external trade, balance of payments, industry, labor, agriculture, transport, household income and expenditure, health, and crime. The bulletin includes general notes on methodology and abbreviations used in the tables.
Christopher Sibona Ph.D. is the Principal Software Engineer at Oracle Corp. Christopher obtained his Ph.D. at the University of Colorado Business School in 2011. His study on why people unfriend on Facebook has helped hundreds of corporations and individuals understand what encourages engagement and what turns people off when marketing on Facebook. This is Christopher’s talk at the January 2011 Emerging Media Conference in San Francisco, CA.
Benjamin Feder went on an internship in Poland from April 28th to May 9th, 2014. He spent time in Krakow visiting places like the Tyskie brewery, Wieliczka salt mine, and Auschwitz concentration camp. Then he traveled to Warsaw and visited companies like Lanxess and Polfrost, as well as the German Embassy. The trip provided an opportunity to learn about Polish culture and business.
Презентация компании LOL&POP. Мы делаем карамель ручной работы. Наши карамельные конфеты ручной работы отличаются вкусом, оригинальностью и тем, что это 100% handmade. Мы можем сделать карамельные конфеты с любой картинкой, надписью, логотипом внутри конфеты. Это отличная идея для подарка, корпоративного промо, свадебных бонбоньерок, а также для оформления кенди бара на любом празднике. Мы просто другие :)
Este documento describe los principios fundamentales del conductismo aplicados al contexto educativo. Explica que el conductismo se centra en el estudio de la conducta observable mediante el método experimental, buscando obtener una representación exacta de la realidad sin la participación subjetiva del investigador. Además, describe cómo el conductismo concibe la enseñanza como la disposición adecuada de las contingencias de refuerzo, y que las metas y objetivos educativos deben ser operativos y observables para facilitar la evaluación del aprendizaje.
The document discusses various types of algorithms:
1) Simple recursive algorithms solve base cases directly and recursively solve simpler subproblems.
2) Backtracking algorithms use depth-first search to test solutions and make choices recursively.
3) Divide and conquer algorithms divide problems into smaller subproblems, solve them recursively, and combine the solutions.
4) Dynamic programming algorithms store and reuse solutions to overlapping subproblems in a bottom-up manner.
This document discusses programming tools used to convert algorithms into computer programs, including flowcharts and pseudocode. It provides examples of using flowcharts and pseudocode to solve problems like determining the number of stamps needed for a letter based on the number of pages, and calculating the average grade for a class. Key programming structures like sequence, decision, and looping are also explained.
This document is a project proposal from Delicious Webdesign to create an e-commerce website for a window blinds company. It outlines the scope of the project, including features for visitors, registered users, and administrators. The site will allow browsing and purchasing of window blinds. It proposes a fixed price model and provides timelines and deliverables. The technical details such as the programming languages and hosting environment are also specified.
Este documento describe los principales componentes y sistemas de motores diésel marinos de dos tiempos. Explica que estos motores generan energía mediante la combustión interna del diésel en cada cilindro, impulsando el pistón. Detalla los sistemas de dos y cuatro tiempos, e identifica ventajas e inconvenientes. Además, describe partes clave como el cigüeñal, pistones, cilindros, culatas y válvulas de escape, así como los tipos de bombas de inyección.
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.
I am Joanna R. I am a Programming Exam Expert at programmingexamhelp.com. I hold a Bachelor of Information Technology from, California Institute of Technology, United States. I have been helping students with their exams for the past 11 years. You can hire me to take your exam in Programming.
Visit programmingexamhelp.com or email support@programmingexamhelp.com. You can also call on +1 678 648 4277 for any assistance with the Programming Exam.
Lecture 5 – Computing with Numbers (Math Lib).pptxjovannyflex
This document provides an overview of numeric data types and operations in Python. It discusses integers and floats, and how they represent numeric values differently. Functions for determining data types like type() and converting between types are presented. Core numeric operations like addition, subtraction, multiplication, division, and modulo are covered. The use of accumulators and loops to calculate factorial and solve other numeric problems is demonstrated. Finally, the math library module is introduced for accessing advanced math functions like square root, trigonometric functions, rounding, and random number generation.
Lecture 5 – Computing with Numbers (Math Lib).pptxjovannyflex
This document provides an overview of numeric data types and operations in Python. It discusses integers and floats, and how they represent numeric values differently. Functions for determining data types like type() and converting between types are presented. Core numeric operations like addition, subtraction, multiplication, division, and modulus are covered. The use of accumulators and loops to calculate factorial and solve other numeric problems is demonstrated. Finally, the math library module is introduced for accessing advanced math functions like square root, trigonometric functions, rounding, and random number generation.
This document discusses structured programming and arrays. It begins by introducing arrays as a way to store multiple values in a structured manner using indices, rather than individual variables. It then discusses how to input and output values from arrays using loops. It also covers multidimensional arrays and declaring arrays. The document provides examples of using arrays to store and manipulate data, such as finding averages and min/max values. It concludes by introducing bubble sort as a way to sort arrays into order.
This document provides information about homework help services and programming homework help. It lists contact details including a phone number and email address to get help with homework queries. It also provides a website URL for programming homework help. The document then presents sample programming homework problems and solutions.
I am Joanna R. I am an Algorithm Exam Expert at programmingexamhelp.com. I hold a Bachelor of Information Technology from, the California Institute of Technology, United States. I have been helping students with their exams for the past 9 years. You can hire me to take your exam in Algorithm.
Visit programmingexamhelp.com or email support@programmingexamhelp.com. You can also call on +1 678 648 4277 for any assistance with the Algorithm Exam.
This document summarizes key concepts from a lecture on decidability in formal language theory:
- Context-free languages (CFLs) are decidable as their acceptability can be determined by a Turing machine that simulates a pushdown automaton.
- The acceptability problem for context-free grammars (ACFG) - determining if a string is in the language of a given CFG - is decidable using a algorithm that lists all derivations.
- This shows that the class of languages recognized by Turing machines is decidable.
- However, diagonalization arguments show that some problems are undecidable, such as the halting problem of determining if an arbitrary Turing machine
The document discusses various sorting algorithms and their time complexities, including:
- Insertion sort runs in O(n^2) time in the worst case.
- Merge sort and heap sort run in O(nlogn) time in the worst case.
- Any comparison-based sorting algorithm requires Ω(nlogn) time.
- Counting sort and radix sort can run in O(n) time by avoiding comparisons, but have additional requirements on the key range.
The discrete logarithm problem (DLP) is the basis for elliptic curve cryptography (ECC) and differs from the integer factorization problem in RSA. In ECC over a finite field, the DLP is to find the exponent that computes one point on the elliptic curve as a multiple of another point, given the curve equation and two points. In RSA, the problem is to find the prime factors of a composite integer. While general algorithms exist to solve both, the DLP in ECC providing equivalent security to RSA requires smaller key sizes, making ECC more efficient.
This document provides an introduction to the theory of computation, including definitions of key concepts like automata theory, symbols, alphabets, strings, languages, and sets. It discusses how automata theory deals with formal models of computation and is used in areas like text processing and programming languages. Mathematical terminology is introduced, such as symbols, alphabets, strings, languages, sets, and the power and Cartesian product of alphabets. Examples are given to illustrate concepts like strings, languages, and valid versus invalid computations based on whether a string is contained within a language.
The ID3 algorithm is a decision tree learning algorithm developed by Ross Quinlan. It builds classification models in the form of a decision tree from a set of labeled training data. The algorithm works by selecting the attribute that is most useful for classifying the training examples at each step, starting with the root node. It then splits the examples into smaller subsets based on the value of that attribute. This process continues recursively on each derived subset until the subsets are pure or until stopping criteria are met. The resulting decision tree can then be used to classify new or unknown data instances.
This document discusses various topics related to digital representation of data including:
1. The differences between FAT32 and NTFS file systems and their advantages and limitations.
2. How data is represented digitally using coding schemes like ASCII and converted between binary and other number systems.
3. An overview of different numbering systems including binary, decimal, octal and hexadecimal; and how to convert between them.
This document discusses various topics related to digital representation of data including:
1. The differences between FAT32 and NTFS file systems and their advantages and limitations.
2. How data is represented digitally using coding schemes like ASCII and converted between binary and other number systems.
3. An overview of different numbering systems including binary, decimal, octal and hexadecimal; and how to convert between them.
The document discusses the benefits of declarative programming using Scala. It provides examples of implementing algorithms and data structures declaratively in Scala. It also discusses the history and future of Scala, as well as how Scala encourages thinking about programs as transformations rather than changes to memory.
The document provides instructions for Quiz 1 of the MIT course 6.006 Introduction to Algorithms. It states that the quiz has 120 minutes and 120 total points. It is closed book except for one crib sheet. Students are to write their solutions in the provided space and show their work for partial credit. The quiz contains 7 problems worth various point values testing topics like asymptotics, recurrences, sorting algorithms, and graph algorithms.
A Gentle Introduction to Coding ... with PythonTariq Rashid
A gentle introduction to coding (programming) for complete beginners. Starting from then basics - electrical wires - proceeding through variables, data structures, loops, functions, and exploring libraries for visualisation and specialist tools. Finally we use flask to make a very simple twitter clone web application.
This document summarizes key concepts in cryptography and number theory relevant to public key cryptography algorithms like RSA. It discusses number theoretic concepts like prime numbers, modular arithmetic, discrete logarithms, and one-way functions. It then provides an overview of the RSA algorithm, explaining how it uses the difficulty of factoring large numbers to enable secure public key encryption and digital signatures.
This document summarizes programming techniques for the HP 33s calculator. It begins by describing the HP 33s' capabilities and limitations compared to the HP 32sII. The main techniques discussed are using as few labels as possible, repeating conditional tests instead of using labels, and storing intermediate results in program memory instead of variables. Bitwise logic operations are implemented by processing truth tables. Sorting and linear least squares routines are also presented. The document emphasizes coding within the 33s' restrictions while taking advantage of its large program memory.
1. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Letters & Numbers: A Vehicle to Illustrate
Mathematical & Computing Fundamentals
Lighthouse Delta 2013
Steve Sugden, QUT
Phil Stocks, Bond University
28/11/2013
2. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
SBS Letters & Numbers
Letters & Numbers is a popular game show on SBS
Fairly new to Australia, but not to Europe
Based on the French des Chi¤res et des Lettres (similar to UK
Countdown)
According to Wikipedia "the oldest TV programme still
broadcast on French Television and one of the longest-running
game shows in the world."
Hosted by Richard Morecroft, David Astle (cryptic crossword
writer) & Lily Serna (UTS maths HDR student)
3. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Anagrams
In the 1990s at Bond I taught the Data Structures subject
about 15 times (MODULA-2, Oberon-2, Delphi)
One of the topics I included was bag - a generalization of set
where elements can occur more than once
Implement by an array of cardinals (frequencies)
Index type of array maps to the set of possible bag elements
For example, a bag of characters (letters) can be used to
check for anagrams or sub-words of a given string
Newspaper anagram-type games are common: The
Australian: "Circuit Breaker", Courier-Mail: "Jumble", GC
Bulletin: "Focus", etc.
Just what is needed for the Letters part of L & N
4. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Letters game in Excel/VBA
I have a strong preference for the Wirth family of languages,
but these are out of vogue nowadays
Not a huge fan of VB, but given my extensive work with
Excel, it made sense for me to get up to speed with VBA
Was not a very di¢ cult task to implement the bag abstraction
in VBA using a frequency array
The code simply computes the bag of the entered word, and
checks every word in the dictionary, looking for sub-bags
5. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Example
Bag for the "word" RETAIONBE:
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
1 1 0 0 2 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0
Some words whose bags are sub-bags of this are BARITONE,
REOBTAIN (the bags for these are equal, i.e., the words are
anagrams):
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
1 1 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0
6. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Numbers game
Given six numbers (operands) and a target number, we seek
an expression which evaluates to the target, and includes at
least two of the six inputs (operands) plus any number of
instances of +, , , /
Operands can be used without replacement (once only) but
operator use is unrestricted
Parentheses may be inserted wherever required
Example: Make the target 547 from a subset of the numbers
4, 7, 8, 10, 50, 75
Solution: 547 = (10 50) + (4 10) + 7?
Is this correct?
7. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
A more interesting challenge
Solving the "numbers" part of the game on a computer is
more challenging
Arithmetic expressions are not linear like strings
They are trees with a recursive structure
8. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Binary tree
A binary tree is usually de…ned in a recursive fashion
Tree ::= empty j (LTree, Root, RTree)
In other words, a binary tree is either empty (nothing at all) or
it consists of a sequence of three objects: the left (sub-)tree,
the root node, the right (sub-)tree
This is a typical BNF (Backus-Naur) recursive syntax
de…nition, and may be compared mathematically to a
recurrence relation
9. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Arithmetic expression tree
"No-one, I think, is in my tree - I mean it must be high or low"
(John Lennon, Strawberry Fields Forever, 1967)
Binary tree associated with an arithmetic expression (AE)
internal nodes: operators
external nodes: operands
Example: AE tree for (2 (8 1) + (3 4))
+
××
−2
8 1
3 4
10. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
In-order tree traversal
PROCEDURE Traverse(t : Tree);
// This mode of traversal generates a normal (in-…x) AE.
// Parens necessary to avoid ambiguity: (2 (8 1) + (3 4))
BEGIN
IF t is not empty THEN
Traverse left subtree of t
Process root node of t
Traverse right subtree of t
END
END Traverse;
3
1
2
5
6
7 9
8
4
+
××
−2
8 1
3 4
11. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Post-order tree traversal
PROCEDURE Traverse(t : Tree);
// This mode of traversal generates a post…x AE
// Parens unnecessary: 281 34 + (HP calculators)
BEGIN
IF t is not empty THEN
Traverse left subtree of t
Traverse right subtree of t
Process root node of t
END
END Traverse;
2
1
5
3
9
6 7
8
4
+
××
−2
8 1
3 4
12. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Operands & rules
Operands may be used only as frequently as they appear
All intermediate results must be positive integers
On the SBS program, operands are split into large and small,
but this does not a¤ect our approach
Operands do not exceed 100 and target is more than 100
this means that at least two operands must be used
13. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Reaching the target
Target may be impossible to reach
Some targets may be reached in many ways
# possible expressions is huge (see later)
Intermediate results could over‡ow or div zero might occur
code to generate trees must check for these
Many trees are equivalent (they generate the same target)
Other equivalences –commutative & associative operators
14. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Catalan number
For n operands we have exactly n 1 operators
Tree has n operand nodes (interior nodes) plus n 1
operators (leaf nodes), giving 2n 1 nodes in total
Start with “interior tree” generated by the n 1 interior
operator nodes
The number of these is given by the Catalan number
Cn 1 =
(2n 2)!
(n 1)!n!
15. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
# operators
There are four possible operators and these may be chosen
n 1 times with replacement
Thus, the number of choices here is 4n 1
16. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
# operands
Now graft n operand nodes onto this tree
Since order matters and operands can only be used once, the
number of ways of doing this is the number of permutations
of n objects chosen from 6
This is
6!
(6 n)!
17. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
The number of trees
The multiplication rule now gives us the total number of trees
with n operands, denoted Tn
Tn =
(2n 2)!
(n 1)!n!
4n 1 6!
(6 n)!
This expression ignores the possibilities of semantically
equivalent trees
Also ignores unsuitable trees such as those which generate
division by 0 or improper …nal or intermediate results
Examples: “5 7” or “25/4” or “10/(3 3)”
Code checks for these and only builds proper trees
18. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
# possible expression trees
Simplifying the quotient Tn/Tn 1 yields a useful recurrence
Thus, the number of trees may also be expressed recursively
as T1 = 6 and
Tn =
8 (2n 3) (7 n)
n
Tn 1 if 2 n 6
n Tn
1 6
2 120
3 3, 840
4 115, 200
5 2, 580, 480
6 30, 965, 760
19. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Algorithm
How to generate a forest this large?
We need to generate all possible expression trees with n
operand nodes and n 1 operator nodes (total 2n 1 nodes)
for 2 n 6
Tree with just one node (one for each of the original 6
operands) give us initial nursery
At any stage, to get all trees with m operand nodes, we graft
together all possible combinations of trees with operand node
counts adding up to m
Need to keep track of which operands have already been used
(cannot use an operand twice)
20. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Create trees by grafting old ones
We need to partition the nodeset into 2 parts
For example, to get all trees with 6 operand nodes we graft:
All trees with 1 node to all with 5 nodes, and
All trees with 2 nodes to all with 4 nodes, and
All trees with 3 nodes to all with 3 nodes
21. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Lily vs machine
L&N Episode; time 3:15
Contestant gets 1 o¤ target
Lily …nds exact answer using 5 operands
Excel model …nds exact answer 2 or 3 sec using 4 operands
ditto for Delphi model, which takes about 100 milliseconds
22. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Delphi version of number game
This was created as the Excel version is slow
It uses the same algorithm but coded into Object Pascal
It runs about 100 times as fast
Much better for the harder problems
23. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Letters and Numbers as a student assignment
In Bond University’s (now defunct) Bachelor of IT, there was
just one mathematics unit, known as Analytical Toolkit
It consists of a typical set of introductory discrete
mathematics topics, ending with a few weeks of very basic
introduction to probability and statistics
At Bond, Sem 1 of 2012, we set an assignment for the
students based on L & N
Mathematical background of a typical student is very poor
Also, most students had no programming experience
How could we present enough background material for the
students …rstly to understand the problem, and secondly to
implement some kind of a solution?
24. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
A reduction of problem scope
We:
reduced the scope of the problem by relaxing the Numbers
Game to 3 operands instead of 6,
cast the problem into an environment where at least some of
the solution logic may be expressed without having to write
code, and
supplied the class with some skeleton code which outlines an
overall solution strategy
These were achieved by putting a reduced version of the
problem into Microsoft Excel 2010 with VBA code and Excel
formulae and tables
For the Letters part of the problem the class was supplied
with public domain word lists, again in Excel
A session on elementary VBA was taught
Based on student feedback, this exercise was a success
25. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Student feedback on the assignment
Although the class was small and the number of responses
even smaller, feedback was uniformly positive
"Very interesting assignment for applying algorithms to solve
problems"
"Found it to be quite fun and enjoyable however due to my
programming background it was easy."
"Although there was a steep learning curve I found this
assignment very interesting"
26. History Numbers game How many trees? Algorithm Lily vs machine L&N assignment Conclusion
Conclusion
The games from Letters & Numbers are shown to be useful
vehicles for showcasing fundamentals of computation and
mathematics
A wide variety of typical CS & Discrete Mathematics concepts
in a degree program apply to the numbers game, making it a
useful teaching vehicle with wide application
Requirements for solving the Numbers Game strongly a¢ rm
why Mathematics is important in a CS or IT degree
We need more maths ambassadors/evangelists to early grades,
where lifelong attitudes to maths are usually formed
We need more people like ***Lily Serna***, an excellent role
model for young girls in particular