Search:

Introducing Algorithms in C : A Step by Step Guide to Algorithms in C. by Manelli, Luciano.;
Description based on publisher supplied metadata and other sources.
Subjects: Electronic books.; Computer algorithms.;
On-line resources: CGCC online access;
unAPI

Essential Maths Skills for AS/a Level Computer Science. by Ellis, Victoria.; Craddock, Gavin.;
Cover -- Book title -- Contents -- Introduction -- 1 Number systems and sets -- Number systems -- Sets -- 2 Number bases -- Converting a base 2 or base 16 number to base 10 -- Converting a base 10 to a base 2 or base 16 number -- 3 Units -- Binary prefixes -- Decimal prefixes -- 4 Arithmetic operations in a programming language -- DIV -- Mod -- Power -- Rounding and truncating -- Logical operations -- 5 Binary numbers -- Signed vs unsigned binary -- Key values for n bits -- Binary addition -- Binary multiplication -- Representing negative numbers -- Fixed point -- Logical shifts -- Bitwise operators -- 6 Floating point data representation -- Floating point binary -- Normalisation -- Range and precision -- Errors -- 7 Representing images, sound and other data -- Bitmap images -- Sound samples -- 8 Boolean algebra -- Logic gates -- Boolean algebra -- Boolean identities and De Morgan's laws -- 9 Vectors -- Vector representation -- Vector addition -- Scalar vector multiplication -- Dot product of two vectors -- Convex combination of two vectors -- 10 Big-O notation and complexity of algorithms -- Functions -- Complexity of algorithms -- Exam-style questions -- Appendix -- Specification cross-reference.If you struggle with binary multiplication, or Big O Notation, this is the book for you. This textbook companion will help improve your essential maths skills for computer science, whichever awarding body specification you're following. You can use it throughout your course, whenever you feel you need some extra help. - Develop your understanding of both maths and computer science with all worked examples and questions within a computer science context - Improve your confidence with a step-by-step approach to every maths skill - Measure your progress with guided and non-guided questions to see how you're improving - Understand where you're going wrong with full worked solutions to every question - Feel confident in expert guidance from experienced teachers and examiners Victoria Ellis and Gavin Craddock, reviewed by Dr Kathleen Maitland, Senior Lecturer in Computing and Director of the SAS Student Academy at Birmingham City University.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Subjects: Electronic books.; Computer algorithms.;
On-line resources: CGCC online access.;
unAPI

Data Structures and Algorithms in Python. by Goodrich, Michael T.; Tamassia, Roberto.; Goldwasser, Michael H.;
Cover -- Title Page -- Copyright Page -- Preface -- Contents -- 1 Python Primer -- 1.1 Python Overview -- 1.1.1 The Python Interpreter -- 1.1.2 Preview of a Python Program -- 1.2 Objects in Python -- 1.2.1 Identifiers, Objects, and the Assignment Statement -- 1.2.2 Creating and Using Objects -- 1.2.3 Python's Built-In Classes -- 1.3 Expressions, Operators, and Precedence -- 1.3.1 Compound Expressions and Operator Precedence -- 1.4 Control Flow -- 1.4.1 Conditionals -- 1.4.2 Loops -- 1.5 Functions -- 1.5.1 Information Passing -- 1.5.2 Python's Built-In Functions -- 1.6 Simple Input and Output -- 1.6.1 Console Input and Output -- 1.6.2 Files -- 1.7 Exception Handling -- 1.7.1 Raising an Exception -- 1.7.2 Catching an Exception -- 1.8 Iterators and Generators -- 1.9 Additional Python Conveniences -- 1.9.1 Conditional Expressions -- 1.9.2 Comprehension Syntax -- 1.9.3 Packing and Unpacking of Sequences -- 1.10 Scopes and Namespaces -- 1.11 Modules and the Import Statement -- 1.11.1 Existing Modules -- 1.12 Exercises -- 2 Object-Oriented Programming -- 2.1 Goals, Principles, and Patterns -- 2.1.1 Object-Oriented Design Goals -- 2.1.2 Object-Oriented Design Principles -- 2.1.3 Design Patterns -- 2.2 Software Development -- 2.2.1 Design -- 2.2.2 Pseudo-Code -- 2.2.3 Coding Style and Documentation -- 2.2.4 Testing and Debugging -- 2.3 Class Definitions -- 2.3.1 Example: CreditCard Class -- 2.3.2 Operator Overloading and Python's Special Methods -- 2.3.3 Example: Multidimensional Vector Class -- 2.3.4 Iterators -- 2.3.5 Example: Range Class -- 2.4 Inheritance -- 2.4.1 Extending the CreditCard Class -- 2.4.2 Hierarchy of Numeric Progressions -- 2.4.3 Abstract Base Classes -- 2.5 Namespaces and Object-Orientation -- 2.5.1 Instance and Class Namespaces -- 2.5.2 Name Resolution and Dynamic Dispatch -- 2.6 Shallow and Deep Copying -- 2.7 Exercises.3 Algorithm Analysis -- 3.1 Experimental Studies -- 3.1.1 Moving Beyond Experimental Analysis -- 3.2 The Seven Functions Used in This Book -- 3.2.1 Comparing Growth Rates -- 3.3 Asymptotic Analysis -- 3.3.1 The "Big-Oh" Notation -- 3.3.2 Comparative Analysis -- 3.3.3 Examples of Algorithm Analysis -- 3.4 Simple Justification Techniques -- 3.4.1 By Example -- 3.4.2 The "Contra" Attack -- 3.4.3 Induction and Loop Invariants -- 3.5 Exercises -- 4 Recursion -- 4.1 Illustrative Examples -- 4.1.1 The Factorial Function -- 4.1.2 Drawing an English Ruler -- 4.1.3 Binary Search -- 4.1.4 File Systems -- 4.2 Analyzing Recursive Algorithms -- 4.3 Recursion Run Amok -- 4.3.1 Maximum Recursive Depth in Python -- 4.4 Further Examples of Recursion -- 4.4.1 Linear Recursion -- 4.4.2 Binary Recursion -- 4.4.3 Multiple Recursion -- 4.5 Designing Recursive Algorithms -- 4.6 Eliminating Tail Recursion -- 4.7 Exercises -- 5 Array-Based Sequences -- 5.1 Python's Sequence Types -- 5.2 Low-Level Arrays -- 5.2.1 Referential Arrays -- 5.2.2 Compact Arrays in Python -- 5.3 Dynamic Arrays and Amortization -- 5.3.1 Implementing a Dynamic Array -- 5.3.2 Amortized Analysis of Dynamic Arrays -- 5.3.3 Python's List Class -- 5.4 Efficiency of Python's Sequence Types -- 5.4.1 Python's List and Tuple Classes -- 5.4.2 Python's String Class -- 5.5 Using Array-Based Sequences -- 5.5.1 Storing High Scores for a Game -- 5.5.2 Sorting a Sequence -- 5.5.3 Simple Cryptography -- 5.6 Multidimensional Data Sets -- 5.7 Exercises -- 6 Stacks, Queues, and Deques -- 6.1 Stacks -- 6.1.1 The Stack Abstract Data Type -- 6.1.2 Simple Array-Based Stack Implementation -- 6.1.3 Reversing Data Using a Stack -- 6.1.4 Matching Parentheses and HTML Tags -- 6.2 Queues -- 6.2.1 The Queue Abstract Data Type -- 6.2.2 Array-Based Queue Implementation -- 6.3 Double-Ended Queues -- 6.3.1 The Deque Abstract Data Type.6.3.2 Implementing a Deque with a Circular Array -- 6.3.3 Deques in the Python Collections Module -- 6.4 Exercises -- 7 Linked Lists -- 7.1 Singly Linked Lists -- 7.1.1 Implementing a Stack with a Singly Linked List -- 7.1.2 Implementing a Queue with a Singly Linked List -- 7.2 Circularly Linked Lists -- 7.2.1 Round-Robin Schedulers -- 7.2.2 Implementing a Queue with a Circularly Linked List -- 7.3 Doubly Linked Lists -- 7.3.1 Basic Implementation of a Doubly Linked List -- 7.3.2 Implementing a Deque with a Doubly Linked List -- 7.4 The Positional List ADT -- 7.4.1 The Positional List Abstract Data Type -- 7.4.2 Doubly Linked List Implementation -- 7.5 Sorting a Positional List -- 7.6 Case Study: Maintaining Access Frequencies -- 7.6.1 Using a Sorted List -- 7.6.2 Using a List with the Move-to-Front Heuristic -- 7.7 Link-Based vs. Array-Based Sequences -- 7.8 Exercises -- 8 Trees -- 8.1 General Trees -- 8.1.1 Tree Definitions and Properties -- 8.1.2 The Tree Abstract Data Type -- 8.1.3 Computing Depth and Height -- 8.2 Binary Trees -- 8.2.1 The Binary Tree Abstract Data Type -- 8.2.2 Properties of Binary Trees -- 8.3 Implementing Trees -- 8.3.1 Linked Structure for Binary Trees -- 8.3.2 Array-Based Representation of a Binary Tree -- 8.3.3 Linked Structure for General Trees -- 8.4 Tree Traversal Algorithms -- 8.4.1 Preorder and Postorder Traversals of General Trees -- 8.4.2 Breadth-First Tree Traversal -- 8.4.3 Inorder Traversal of a Binary Tree -- 8.4.4 Implementing Tree Traversals in Python -- 8.4.5 Applications of Tree Traversals -- 8.4.6 Euler Tours and the Template Method Pattern -- 8.5 Case Study: An Expression Tree -- 8.6 Exercises -- 9 Priority Queues -- 9.1 The Priority Queue Abstract Data Type -- 9.1.1 Priorities -- 9.1.2 The Priority Queue ADT -- 9.2 Implementing a Priority Queue -- 9.2.1 The Composition Design Pattern.9.2.2 Implementation with an Unsorted List -- 9.2.3 Implementation with a Sorted List -- 9.3 Heaps -- 9.3.1 The Heap Data Structure -- 9.3.2 Implementing a Priority Queue with a Heap -- 9.3.3 Array-Based Representation of a Complete Binary Tree -- 9.3.4 Python Heap Implementation -- 9.3.5 Analysis of a Heap-Based Priority Queue -- 9.3.6 Bottom-Up Heap Construction -- 9.3.7 Python's heapq Module -- 9.4 Sorting with a Priority Queue -- 9.4.1 Selection-Sort and Insertion-Sort -- 9.4.2 Heap-Sort -- 9.5 Adaptable Priority Queues -- 9.5.1 Locators -- 9.5.2 Implementing an Adaptable Priority Queue -- 9.6 Exercises -- 10 Maps, Hash Tables, and Skip Lists -- 10.1 Maps and Dictionaries -- 10.1.1 The Map ADT -- 10.1.2 Application: Counting Word Frequencies -- 10.1.3 Python's MutableMapping Abstract Base Class -- 10.1.4 Our MapBase Class -- 10.1.5 Simple Unsorted Map Implementation -- 10.2 Hash Tables -- 10.2.1 Hash Functions -- 10.2.2 Collision-Handling Schemes -- 10.2.3 Load Factors, Rehashing, and Efficiency -- 10.2.4 Python Hash Table Implementation -- 10.3 Sorted Maps -- 10.3.1 Sorted Search Tables -- 10.3.2 Two Applications of Sorted Maps -- 10.4 Skip Lists -- 10.4.1 Search and Update Operations in a Skip List -- 10.4.2 Probabilistic Analysis of Skip Lists -- 10.5 Sets, Multisets, and Multimaps -- 10.5.1 The Set ADT -- 10.5.2 Python's MutableSet Abstract Base Class -- 10.5.3 Implementing Sets, Multisets, and Multimaps -- 10.6 Exercises -- 11 Search Trees -- 11.1 Binary Search Trees -- 11.1.1 Navigating a Binary Search Tree -- 11.1.2 Searches -- 11.1.3 Insertions and Deletions -- 11.1.4 Python Implementation -- 11.1.5 Performance of a Binary Search Tree -- 11.2 Balanced Search Trees -- 11.2.1 Python Framework for Balancing Search Trees -- 11.3 AVL Trees -- 11.3.1 Update Operations -- 11.3.2 Python Implementation -- 11.4 Splay Trees -- 11.4.1 Splaying.11.4.2 When to Splay -- 11.4.3 Python Implementation -- 11.4.4 Amortized Analysis of Splaying -- 11.5 (2,4) Trees -- 11.5.1 Multiway Search Trees -- 11.5.2 (2,4)-Tree Operations -- 11.6 Red-Black Trees -- 11.6.1 Red-Black Tree Operations -- 11.6.2 Python Implementation -- 11.7 Exercises -- 12 Sorting and Selection -- 12.1 Why Study Sorting Algorithms? -- 12.2 Merge-Sort -- 12.2.1 Divide-and-Conquer -- 12.2.2 Array-Based Implementation of Merge-Sort -- 12.2.3 The Running Time of Merge-Sort -- 12.2.4 Merge-Sort and Recurrence Equations -- 12.2.5 Alternative Implementations of Merge-Sort -- 12.3 Quick-Sort -- 12.3.1 Randomized Quick-Sort -- 12.3.2 Additional Optimizations for Quick-Sort -- 12.4 Studying Sorting through an Algorithmic Lens -- 12.4.1 Lower Bound for Sorting -- 12.4.2 Linear-Time Sorting: Bucket-Sort and Radix-Sort -- 12.5 Comparing Sorting Algorithms -- 12.6 Python's Built-In Sorting Functions -- 12.6.1 Sorting According to a Key Function -- 12.7 Selection -- 12.7.1 Prune-and-Search -- 12.7.2 Randomized Quick-Select -- 12.7.3 Analyzing Randomized Quick-Select -- 12.8 Exercises -- 13 Text Processing -- 13.1 Abundance of Digitized Text -- 13.1.1 Notations for Strings and the Python str Class -- 13.2 Pattern-Matching Algorithms -- 13.2.1 Brute Force -- 13.2.2 The Boyer-Moore Algorithm -- 13.2.3 The Knuth-Morris-Pratt Algorithm -- 13.3 Dynamic Programming -- 13.3.1 Matrix Chain-Product -- 13.3.2 DNA and Text Sequence Alignment -- 13.4 Text Compression and the Greedy Method -- 13.4.1 The Huffman Coding Algorithm -- 13.4.2 The Greedy Method -- 13.5 Tries -- 13.5.1 Standard Tries -- 13.5.2 Compressed Tries -- 13.5.3 Suffix Tries -- 13.5.4 Search Engine Indexing -- 13.6 Exercises -- 14 Graph Algorithms -- 14.1 Graphs -- 14.1.1 The Graph ADT -- 14.2 Data Structures for Graphs -- 14.2.1 Edge List Structure -- 14.2.2 Adjacency List Structure.14.2.3 Adjacency Map Structure.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Subjects: Electronic books.; Computer algorithms;
On-line resources: CGCC online access;
unAPI

Turing's Vision : The Birth of Computer Science. by Bernhardt, Chris.;
Contents -- Acknowledgments -- Introduction -- 1 Background -- Mathematical Certainty -- Boole's Logic -- Mathematical Logic -- Securing the Foundations of Mathematics -- Hilbert's Approach -- G¨odel's Results -- Turing's Results -- 2 Some Undecidable Decision Problems -- Emil Post -- Post's Correspondence Problem -- Hilbert's Tenth Problem -- The Halting Problem -- Back to Turing at Cambridge -- 3 Finite Automata -- Introduction -- Finite Automata -- Our First Machine -- Alphabets and Languages -- Finite Automata and Answering Questions -- Omitting Traps from Diagrams -- Some Basic Facts -- Regular Expressions -- Limitations of Finite Automata -- Tapes and Configurations -- Connection to the Correspondence Problem -- 4 Turing Machines -- Examples of Turing Machines -- Computable Functions and Calculations -- Church-Turing Thesis -- Computational Power -- Machines That Don't Halt -- 5 Other Systems for Computation -- The Lambda Calculus -- Tag Systems -- One-Dimensional Cellular Automata -- 6 Encodings and the Universal Machine -- A Method of Encoding Finite Automata -- Universal Machines -- Construction of Universal Machines -- Modern Computers Are Universal Machines -- Von Neumann Architecture -- Random Access Machines -- RAMs Can Be Emulated by Turing Machines -- Other Universal Machines -- What Happens When We Input (M) into M? -- 7 Undecidable Problems -- Proof by Contradiction -- Russell's Barber -- Finite Automata That Do Not Accept Their Encodings -- Turing Machines That Do Not Accept Their Encodings -- Does a Turing Machine Diverge on Its Encoding? Is Undecidable -- The Acceptance, Halting, and Blank Tape Problems -- An Uncomputable Function -- Turing's Approach -- 8 Cantor's DiagonalizationArguments -- Georg Cantor 1845-1918 -- Cardinality -- Subsets of the Rationals That Have the Same Cardinality -- Hilbert's Hotel.Subtraction Is Not Well-Defined -- General Diagonal Argument -- The Cardinality of the Real Numbers -- The Diagonal Argument -- The Continuum Hypothesis -- The Cardinality of Computations -- Computable Numbers -- A Non-Computable Number -- There Is a Countable Number of Computable Numbers -- Computable Numbers Are Not Effectively Enumerable -- 9 Turing's Legacy -- Turing at Princeton -- Second World War -- Development of Computers in the 1940s -- The Turing Test -- Downfall -- Apology and Pardon -- Further Reading -- Notes -- Bibliography -- Index.Turing's fascinating and remarkable theory, which now forms the basis of computer science, explained for the general reader.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Subjects: Electronic books.; Computer algorithms - History.;
On-line resources: CGCC online access;
unAPI

Turing's Vision : The Birth of Computer Science. by Bernhardt, Chris.;
Intro -- Contents -- Acknowledgments -- Introduction -- 1 Background -- Mathematical Certainty -- Boole's Logic -- Mathematical Logic -- Securing the Foundations of Mathematics -- Hilbert's Approach -- G¨odel's Results -- Turing's Results -- 2 Some Undecidable Decision Problems -- Emil Post -- Post's Correspondence Problem -- Hilbert's Tenth Problem -- The Halting Problem -- Back to Turing at Cambridge -- 3 Finite Automata -- Introduction -- Finite Automata -- Our First Machine -- Alphabets and Languages -- Finite Automata and Answering Questions -- Omitting Traps from Diagrams -- Some Basic Facts -- Regular Expressions -- Limitations of Finite Automata -- Tapes and Configurations -- Connection to the Correspondence Problem -- 4 Turing Machines -- Examples of Turing Machines -- Computable Functions and Calculations -- Church-Turing Thesis -- Computational Power -- Machines That Don't Halt -- 5 Other Systems for Computation -- The Lambda Calculus -- Tag Systems -- One-Dimensional Cellular Automata -- 6 Encodings and the Universal Machine -- A Method of Encoding Finite Automata -- Universal Machines -- Construction of Universal Machines -- Modern Computers Are Universal Machines -- Von Neumann Architecture -- Random Access Machines -- RAMs Can Be Emulated by Turing Machines -- Other Universal Machines -- What Happens When We Input (M) into M? -- 7 Undecidable Problems -- Proof by Contradiction -- Russell's Barber -- Finite Automata That Do Not Accept Their Encodings -- Turing Machines That Do Not Accept Their Encodings -- Does a Turing Machine Diverge on Its Encoding? Is Undecidable -- The Acceptance, Halting, and Blank Tape Problems -- An Uncomputable Function -- Turing's Approach -- 8 Cantor's DiagonalizationArguments -- Georg Cantor 1845-1918 -- Cardinality -- Subsets of the Rationals That Have the Same Cardinality -- Hilbert's Hotel.Subtraction Is Not Well-Defined -- General Diagonal Argument -- The Cardinality of the Real Numbers -- The Diagonal Argument -- The Continuum Hypothesis -- The Cardinality of Computations -- Computable Numbers -- A Non-Computable Number -- There Is a Countable Number of Computable Numbers -- Computable Numbers Are Not Effectively Enumerable -- 9 Turing's Legacy -- Turing at Princeton -- Second World War -- Development of Computers in the 1940s -- The Turing Test -- Downfall -- Apology and Pardon -- Further Reading -- Notes -- Bibliography -- Index.Turing's fascinating and remarkable theory, which now forms the basis of computer science, explained for the general reader.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Subjects: Electronic books.; Computer algorithms - History.;
On-line resources: CGCC online access;
unAPI

Numerical Algorithms : Methods for Computer Vision, Machine Learning, and Graphics. by Solomon, Justin.;
Cover -- Accessing the E-book edition -- Dedication -- Contents -- Preface -- Acknowledgments -- Section I: Preliminaries -- Chapter 1: Mathematics Review -- Chapter 2: Numerics and Error Analysis -- Section II: Linear Algebra -- Chapter 3: Linear Systems and the LU Decomposition -- Chapter 4: Designing and Analyzing Linear Systems -- Chapter 5: Column Spaces and QR -- Chapter 6: Eigenvectors -- Chapter 7: Singular Value Decomposition -- Section III: Nonlinear Techniques -- Chapter 8: Nonlinear Systems -- Chapter 9: Unconstrained Optimization -- Chapter 10: Constrained Optimization -- Chapter 11: Iterative Linear Solvers -- Chapter 12: Specialized Optimization Methods -- Section IV: Functions, Derivatives, and Integrals -- Chapter 13: Interpolation -- Chapter 14: Integration and Differentiation -- Chapter 15: Ordinary Differential Equations -- Chapter 16: Partial Differential Equations -- Bibliography -- Back Cover.Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.The book covers a wide range of topics-from numerical linear algebra to optimization and differential equations-focusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students' intuition while introducing extensions of the basic material.The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Subjects: Electronic books.; Computer algorithms;
On-line resources: CGCC online access;
unAPI

Numerical Algorithms : Methods for Computer Vision, Machine Learning, and Graphics. by Solomon, Justin.;
Cover -- Accessing the E-book edition -- Dedication -- Contents -- Preface -- Acknowledgments -- Section I: Preliminaries -- Chapter 1: Mathematics Review -- Chapter 2: Numerics and Error Analysis -- Section II: Linear Algebra -- Chapter 3: Linear Systems and the LU Decomposition -- Chapter 4: Designing and Analyzing Linear Systems -- Chapter 5: Column Spaces and QR -- Chapter 6: Eigenvectors -- Chapter 7: Singular Value Decomposition -- Section III: Nonlinear Techniques -- Chapter 8: Nonlinear Systems -- Chapter 9: Unconstrained Optimization -- Chapter 10: Constrained Optimization -- Chapter 11: Iterative Linear Solvers -- Chapter 12: Specialized Optimization Methods -- Section IV: Functions, Derivatives, and Integrals -- Chapter 13: Interpolation -- Chapter 14: Integration and Differentiation -- Chapter 15: Ordinary Differential Equations -- Chapter 16: Partial Differential Equations -- Bibliography -- Back Cover.Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.The book covers a wide range of topics-from numerical linear algebra to optimization and differential equations-focusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students' intuition while introducing extensions of the basic material.The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Subjects: Electronic books.; Computer algorithms;
On-line resources: CGCC online access;
unAPI

Data Versus Democracy : How Big Data Algorithms Shape Opinions and Alter the Course of History. by Shaffer, Kris.;
Description based on publisher supplied metadata and other sources.
Subjects: Electronic books.; Big data..; Computer algorithms.;
On-line resources: CGCC online access;
unAPI

Ada Lovelace and Computer Algorithms. by Labrecque, Ellen.(DLC)1589327;
Cover -- Table of Contents -- A Woman -- An Idea -- A Legacy -- Glossary -- Find Out More -- Index.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Subjects: Electronic books.;
On-line resources: CGCC online access;
unAPI

Frontiers in Algorithmics : Third Joint International Conference, FAW-AAIM 2013, Dalian, China, June 26-28, 2013. Proceedings. by Fellows, Michael.; Tan, Xuehou.; Zhu, Binhai.;
Intro -- Preface -- Organization -- Table of Contents -- Invited Lectures -- The Square Root Phenomenon in Planar Graphs -- An Algorithm for Determining Whether a Pair of Polygons Is Reversible -- References -- Contributed Papers -- Disjoint Small Cycles in Graphs and Bipartite Graphs -- 1 Introduction -- 2 Lemmas -- 3 Proof of Theorem 1.3 -- 4 Bipartite Graph -- 5 Conjectures -- References -- An Algorithm for Listing All Minimal 2-Dominating Sets of a Tree -- 1 Introduction -- 2 Results -- References -- Algorithms for Testing Length Four Permutations -- 1 Introduction -- 2 Preliminaries -- 3 Length Four Permutations Except 1324 -- 4 Permutation 1324 -- 5 Concluding Remarks -- References -- Partial Degree Bounded Edge Packing Problem with Arbitrary Bounds -- 1 Introduction -- 1.1 Related Work -- 1.2 Our Contribution -- 2 Approximation Algorithms for the Unweighted Case -- 2.1 Edge Addition Based Algorithm -- 2.2 Edge Deletion Based Algorithm -- 2.3 LP Based Algorithm -- 2.4 Algorithm for IP2 -- 3 Approximation Algorithm for the Weighted Case -- 3.1 The Algorithm -- 4 Exact Algorithm for Trees -- References -- Faster Exact Computation of rSPR Distance -- 1 Introduction -- 2 Preliminaries -- 3 Sketch of Whidden et al.'s Algorithm for rSPR Distance -- 4 Ideas for Improving Whidden et al.'s Algorithm -- 5 Details of Step 5 in Section 4 When -- Is a Leaf -- 6 The Worst Case in Our Algorithm -- 7 Performance Comparison -- References -- Arbitrated Quantum Signature Schemes: Attacks and Security -- 1 Introduction -- 2 Quantum One-Time Pads Encryption -- 3 Attacks on Arbitrated Quantum Signature -- 3.1 Bob's Forgery Attacks -- 3.2 Alice's Disavowal Attacks -- 3.3 The Reasons for AQS Scheme Suffered Attacks -- 3.4 Other Attacks -- 4 An AQS Scheme -- 5 Security Analyses -- 5.1 The Method to Resolve Disputes.5.2 Impossibility of Disavowal by the Signatory and the Receiver -- 5.3 Impossibility of Forgery -- 5.4 Other Discussion -- 6 Conclusions -- References -- Randomized Algorithms for Removable Online Knapsack Problems -- 1 Introduction -- 2 Unweighted Knapsack Problem -- 2.1 Upper Bound -- 2.2 Lower Bound -- 3 Weighted Knapsack Problem -- 3.1 Upper Bound -- 3.2 Lower Bound -- References -- An Exact Algorithm for Maximum Independent Set in Degree-5 Graphs -- 1 Introduction -- 2 Notation System -- 3 Reduction Rules -- 4 Properties of Vertex-Cuts with Size at Most 2 -- 5 Branching Rules -- 6 The Algorithm and Results -- 6.1 Framework for Analysis -- 6.2 The Algorithm -- 6.3 The Result -- 7 Framework for the Proof of Lemma 6 -- References -- FWLS: A Local Search for Graph Coloring -- 1 Introduction -- 2 Preliminary -- 3 Focused Walk Based Local Search for Graph Coloring -- 3.1 Basic Notation and Definitions -- 3.2 The FWLS Algorithm -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- A One-Vertex Decomposition Algorithm for Generating Algebraic Expressions of Square Rhomboids -- 1 Introduction -- 2 A One-Vertex Decomposition Method (1-VDM) -- 3 A One-Vertex Decomposition Algorithm (1-VDA) -- 4 Comparison of 1-VDA with Other Algorithms -- 5 Conclusions and Future Work -- References -- Monomial Testing and Applications -- 1 Introduction -- 2 Notations and Definitions -- 3 A New Transformation -- 3.1 A New Circuit Reconstruction Method -- 3.2 Variable Replacements -- 4 A Faster Randomized Algorithm -- 5 A Deterministic Algorithm via Derandomization -- 6 Applications -- 6.1 Allowing Overlapping in -- 6.2 Testing Non-Simple -- 6.3 A Generalized -- References -- The Optimal Rescue Path Set Problem in Undirected Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Properties of Optimal Rescue Path Set -- 3.1 Minimum Joint Replacement Path Set.4 Optimal Rescue Path Set -- 5 Conclusion -- References -- Expected Computations on Color Spanning Sets -- 1 Introduction -- 2 Problem Definition -- 3 Algorithm for Problem 1 -- 4 Algorithm for Problem 2 -- 5 Algorithm for Problem 3 -- 6 Algorithm for Problem 4 -- 7 Algorithm for Problem 5 -- 8 Conclusions -- References -- Independent Domination: Reductions from Circular- and Triad-Convex Bipartite Graphs to Convex Bipartite Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Reduction from Circular-Convex Bipartite Graphs -- 4 Reduction from Triad-Convex Bipartite Graphs -- 5 Concluding Remarks -- References -- Spanning Distribution Trees of Graphs -- 1 Introduction -- 2 NP-Completeness -- 3 Pseudo-polynomial Algorithm -- 3.1 Outline of Algorithm -- 3.2 Definitions of Functions -- 3.3 Algorithm -- 3.4 Computation Time -- 4 Concluding Remarks -- References -- A Cutting Plane Heuristic Algorithm for the Time Dependent Chinese Postman Problem -- 1 Introduction -- 2 Problem Formulation -- 3 Results on TDCPP Polyhedron -- 3.1 Affinely Independent TDCPP-Tours in CA Polytope -- 3.2 Dimension of CA Polytope -- 3.3 Facet Defining Inequalities for the CA Polytope -- 3.4 Further Strong Valid Inequalities for TDCPP -- 4 Description of the Cutting Plane Heuristic Algorithm -- 5 Computational Results -- 6 Conclusion -- References -- Zero-Visibility Cops and Robber Game on a Graph -- 1 Introduction -- 2 Zero-Visibility Cops and Robber -- 3 Pathwidth and the Zero-Visibility Copnumber -- 4 Constructions -- 5 Comparisons between the Zero-Visibility Copnumbers and the Pathwidth of a Graph -- 6 Conclusion -- References -- On (k, l)-Graph Sandwich Problems -- 1 Introduction -- 2 The Strongly Chordal-(2, 1) Graph Sandwich Problem -- 2.1 The Strongly Chordal-(k, l) Graph Sandwich Problem, k ≥ 2, l ≥ 1 -- 3 Graph Sandwich Problems with Boundary Conditions.3.1 (Poly-Color(k), (k, l), Polynomial Number of Maximal Cliques)- sp -- 4 Conclusion -- References -- Fixed-Parameter Tractability ofWorkflow Satisfiability in the Presence of Seniority Constraints -- 1 Introduction -- 2 Preliminaries -- 3 FPT Algorithm for Bounded Treewidth -- 4 Hardness -- 5 Concluding Remarks -- References -- Two-Round Discrete Voronoi Game along a Line -- 1 Introduction -- 2 Lower Bounds -- 3 Optimal Strategies of P1 and P2 While Placing Second Facilities -- 4 Optimal Strategy of P2 While Placing the First Facility -- 5 Optimal Strategy of P1 While Placing the First Facility -- References -- Inverse Maximum Flow Problems under the Combining Norms -- 1 Introduction -- 2 Inverse Maximum Flow Problem under the Sum-Type Combining Norms -- 3 Inverse Maximum Flow Problem under the Bottleneck-Type Combining Norms -- 4 Concluding Remarks -- References -- The Edge-Recoloring Cost of Paths and Cycles in Edge-Colored Graphs and Digraphs -- 1 Introduction, Notation and Terminology -- 2 Construction of Monochromatic Structures in -- 3 Construction of PEC Paths, Trails and Cycles -- 3.1 The Non-Oriented Case -- 3.2 The Oriented Case -- 4 Destruction of pec Cycles and pec Closed Trails in Gc -- References -- A Cost-Efficient Scheduling Algorithm for Traffic Grooming -- 1 Introduction -- 2 Problem Formulation -- 3 The Approximation Algorithm: Longest Link Interval First -- 4 Conclusion -- References -- Strategies of Groups Evacuation from a Convex Region in the Plane -- 1 Introduction -- 2 Preliminary -- 3 Lower Bound -- 4 Scenario 1: General Plane -- 5 Scenario 2: Plane with Grid Network -- 5.1 Analysis of Strategy with Boundary Information -- 5.2 Analysis of Strategies without Boundary Information -- 6 Comparison -- 6.1 Comparison of Different Scenarios -- 6.2 Comparison of Different Cases -- 7 Conclusion -- References.Kernelization and Lower Bounds of the Signed Domination Problem -- 1 Introduction -- 2 Preliminaries -- 3 The Complexity of -- 4 Kernelizations of -- 4.1 Kernel for General Graphs -- 4.2 Kernel for Planar Graphs -- 4.3 Kernel for d-Partite Graphs -- 4.4 Kernel for Bounded-Degree Graphs -- 4.5 Kernel for r-Regular Graphs -- 4.6 Kernel for Grid Graphs -- 5 Conclusion -- References -- On Edge-Independent Sets -- 1 Introduction -- 2 Rankwidth of Edge-Clique Graphs of Cocktail Parties -- 3 Algorithms for Distance-Hereditary Graphs and Related Graph Classes -- 3.1 Cographs -- 3.2 Distance-Hereditary Graphs -- 3.3 P4-Sparse Graphs -- 4 Algorithms for Planar Graphs -- 4.1 Planar Graphs without Triangle Separator -- 5 NP-Completeness for Graphs without Odd Wheels -- 6 Concluding Remarks -- References -- On the Complexity of Approximate Sum of Sorted List -- 1 Introduction -- 2 Algorithm for Approximate Sum of Sorted List -- 2.1 Approximate Region -- 2.2 Approximate Sum -- 3 Lower Bounds -- 3.1 Lower Bound for Computing Approximate Sum -- 3.2 Lower Bound for Computing Approximate Region -- 3.3 Lower Bound for Sorted List with Negative Elements -- 4 Conclusions and Open Problems -- References -- Large Hypertree Width for Sparse Random Hypergraphs -- 1 Introduction -- 2 Preliminaries -- 3 Lower Bound on Hypertree Width for Random Hypergraphs -- 4 Conclusion and Open Problems -- References -- On Perfect Absorbants in De Bruijn Digraphs -- 1 Introduction -- 2 Preliminaries -- 3 Perfect Absorbants of GB(n,d) -- 4 GB(n,d) with d = 2c(k−1)+1 -- 5 Concluding Remarks -- References -- Multi-Multiway Cut Problem on Graphs of Bounded Branch Width -- 1 Introduction -- 2 Preliminaries -- 2.1 Graph -- 2.2 Branch Decomposition -- 2.3 Logic -- 3 Logical Approach -- 4 Dynamic Programming Approach -- 4.1 Computing -- 4.2 Proof of the Correctness -- 5 Conclusion -- References.Bi-criteria Scheduling on Multiple Machines Subject to Machine Availability Constraints.Description based on publisher supplied metadata and other sources.
Subjects: Electronic books.; Computer algorithms-Congresses.;
On-line resources: CGCC online access;
unAPI