Algorithmic Analysis of Data Structures: Difference between revisions
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To perform a big O complexity analysis, we utilize random but seeded inputs that statistically sample the input space in a representative way. This ensures we aren't getting stuck measuring the runtime of a worst-case or best-case, and thus getting a skewed view of the algorithm. | To perform a big O complexity analysis, we utilize random but seeded inputs that statistically sample the input space in a representative way. This ensures we aren't getting stuck measuring the runtime of a worst-case or best-case, and thus getting a skewed view of the algorithm. | ||
Different kinds of timing tests, like adding and removing at a high, balanced rate and having a near-empty queue with high throughput. | |||
Testing different operation times versus N, to plot on graphs and verify our code is correct. | |||
===Memory usage=== | ===Memory usage=== | ||
Revision as of 01:06, 5 June 2017
Algorithmic analysis
While each data container is implemented differently, due to different tradeoffs between storage space and algorithmic complexity, it is useful to define a set of core tests for algorithmic analysis of data structures.
We begin with lists, which are arguably the simplest.
Big O complexity analysis
To perform a big O complexity analysis, we utilize random but seeded inputs that statistically sample the input space in a representative way. This ensures we aren't getting stuck measuring the runtime of a worst-case or best-case, and thus getting a skewed view of the algorithm.
Different kinds of timing tests, like adding and removing at a high, balanced rate and having a near-empty queue with high throughput.
Testing different operation times versus N, to plot on graphs and verify our code is correct.
Memory usage
No way to measure memory usage of objects in-memory, we are left on our own to performing memory profiling.
Flags
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Data Structures Part of Computer Science Notes
This is the staging ground for computer science notes as part of the 2017 CS Study Plan.
Classes of data structures: Abstract Data Types Array-based and Link-based memory management: ArrayLists and Linked Lists Algorithmic Analysis of Data Structures: Algorithmic Analysis of Data Structures Advanced data structures: Advanced Data Structures
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Arrays Part of Computer Science Notes
Series on Data Structures Python: Arrays/Python · Arrays/Python/Sizeof · Arrays/Python/AppendCost · Arrays/Python/CaesarCipher · Arrays/Python/CompactArrays · Arrays/Python/DynamicArray Java: Arrays/Java · Arrays/Java/CaesarCipher · Arrays/Java/FisherYates · Arrays/Java/PythonList · Arrays/Java/Repeatedly_Remove Categories: Category:Python Arrays
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Stacks and Queues Part of Computer Science Notes
Series on Data Structures
Stacks and Queues: Python StacksQueues/Python · StacksQueues/Python/ArrayStack · StacksQueues/Python/ArrayQueue · StacksQueues/Python/ArrayDeque StacksQueues/Python/LinkedStack
Stacks and Queues: Java StacksQueues/Java · StacksQueues/Java/ArrayStack · StacksQueues/Java/ArrayQueue · StacksQueues/Java/ArrayQueueFS · StacksQueues/Java/ArrayDeque StacksQueues/Java/LinkedStack · StacksQueues/Java/LinkedQueue · StacksQueues/Java/LinkedDeque
Applications Postfix_Expressions#Stacks · StacksQueues/Subsets · StacksQueues/Subsets/Java
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Priority Queues and Heaps Part of Computer Science Notes
Series on Data Structures
Java: Priority Queues/Java · Priority Queues/ADT · Priority Queues/Sorted · Priority Queues/Unsorted Performance: Priority Queues/Timing and Performance Applications: Maximum Oriented Priority Queue · Priority Queues/Stack
Priority Queues/Heap · Priority Queues/Java · Priority Queues/Comparators
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Linked List Part of Computer Science Notes
Series on Data Structures Java: Linked Lists/Java · Linked Lists/Java/Single · Linked Lists/Java/Double · Linked Lists/Java/Circular Performance: Linked Lists/Java/Timing · Linked Lists/Java/Reverse Python: Linked Lists/Python · Linked Lists/Python/Single
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Trees Part of Computer Science Notes
Series on Data Structures Abstract data type: Trees/ADT Concrete implementations: Trees/LinkedTree · Trees/ArrayTree · SimpleTree
Tree Traversal Preorder traversal: Trees/Preorder Postorder traversal: Trees/Postorder In-Order traversal: Binary Trees/Inorder Breadth-First Search: BFS Breadth-First Traversal: BFT Depth-First Search: DFS Depth-First Traversal: DFT OOP Principles for Traversal: Tree Traversal/OOP · Tree Traversal/Traversal Method Template Tree operations: Trees/Operations Performance · Trees/Removal
Tree Applications Finding Minimum in Log N Time: Tree/LogN Min Search
Abstract data type: Binary Trees/ADT Concrete implementations: Binary Trees/LinkedBinTree · Binary Trees/ArrayBinTree Binary Trees/Cheat Sheet · Binary Trees/OOP · Binary Trees/Implementation Notes
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Search Trees Part of Computer Science Notes
Series on Data Structures
Binary Search Trees · Balanced Search Trees Trees/OOP · Search Trees/OOP · Tree Traversal/OOP · Binary Trees/Inorder
(Note that heaps are also value-sorting trees with minimums at the top. See Template:PriorityQueuesFlag and Priority Queues.)
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Maps and Dictionaries Part of Computer Science Notes
Series on Data Structures
Maps/Dictionaries Maps · Maps/ADT · Maps in Java · Maps/OOP · Maps/Operations and Performance Map implementations: Maps/AbstractMap · Maps/UnsortedArrayMap · Maps/SortedArrayMap Dictionary implementations: Dictionaries/LinkedDict · Dictionaries/ArrayDict
Hashes Hash Maps/OOP · Hash Maps/Operations and Performance Hash Maps/Dynamic Resizing · Hash Maps/Collision Handling with Chaining Hash functions: Hash Functions · Hash Functions/Cyclic Permutation Hash map implementations: Hash Maps/AbstractHashMap · Hash Maps/ChainedHashMap
Skip Lists · Java/ConcurrentSkipList · Java implementations: SkipList
Sets Sets · Sets/ADT · Sets in Java · Sets/OOP · Multisets
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