Powerpoint
... Iterator ix = x.iterator(); constructs and initializes an iterator to examine the elements of x; constructed iterator is assigned to ix ...
... Iterator ix = x.iterator(); constructs and initializes an iterator to examine the elements of x; constructed iterator is assigned to ix ...
Stacks
... New nodes can be added and removed only at the top Similar to a pile of dishes Last-in, first-out (LIFO) Bottom of stack indicated by a link member to NULL Constrained version of a linked list ...
... New nodes can be added and removed only at the top Similar to a pile of dishes Last-in, first-out (LIFO) Bottom of stack indicated by a link member to NULL Constrained version of a linked list ...
Priority Queues 1. Priority Queues 2. PQueue Specification
... have to be done ⇒ O(log n). • examine: The root of the binary tree is the entry with the largest key value. Hence, merely return the root of the tree ⇒ O(1). • dequeue: In this case, we must also delete the root node from the tree and then restore the heap property. This can be achieved by moving th ...
... have to be done ⇒ O(log n). • examine: The root of the binary tree is the entry with the largest key value. Hence, merely return the root of the tree ⇒ O(1). • dequeue: In this case, we must also delete the root node from the tree and then restore the heap property. This can be achieved by moving th ...
7. Full-Text Indexes in External Memory
... set of suffixes of T (see Fig. 7.2). With suffix trees, it is customary to add a sentinel character $ to the end of each string in T to make the set of suffixes prefix free. String matching (prefix searching) in a suffix tree is done by walking down the tree along the path labeled by the pattern (see Fig. 7.2 ...
... set of suffixes of T (see Fig. 7.2). With suffix trees, it is customary to add a sentinel character $ to the end of each string in T to make the set of suffixes prefix free. String matching (prefix searching) in a suffix tree is done by walking down the tree along the path labeled by the pattern (see Fig. 7.2 ...
pptx
... • Maintain as unordered list – add() put new element at front – O(1) – poll() must search the list – O(n) – peek() must search the list – O(n) • Maintain as ordered list – add() must search the list – O(n) – poll() min element at front – O(1) – peek() O(1) Can we do better? ...
... • Maintain as unordered list – add() put new element at front – O(1) – poll() must search the list – O(n) – peek() must search the list – O(n) • Maintain as ordered list – add() must search the list – O(n) – poll() min element at front – O(1) – peek() O(1) Can we do better? ...
cp unit 5
... sequence. Under the simplest form, each node is composed of a datum and a reference (in other words, a link) to the next node in the sequence; more complex variants add additional links. This structure allows for efficient insertion or removal of elements from any position in the sequence. The princ ...
... sequence. Under the simplest form, each node is composed of a datum and a reference (in other words, a link) to the next node in the sequence; more complex variants add additional links. This structure allows for efficient insertion or removal of elements from any position in the sequence. The princ ...
Questions
... a. What is algorithmic complexity? b. List 2 reasons benchmarking is better than analyzing complexity c. What is the difference between best case, worst case, and average case? d. What is a recurrence relation? D. Big-O notation and complexity a. What does the Big-O notation represent? b. Why are O( ...
... a. What is algorithmic complexity? b. List 2 reasons benchmarking is better than analyzing complexity c. What is the difference between best case, worst case, and average case? d. What is a recurrence relation? D. Big-O notation and complexity a. What does the Big-O notation represent? b. Why are O( ...
Midterm
... you to identify the following tasks as either possible (with algorithms and data structures introduced in this course), impossible, or an open research problem. You may use each letter once, more than once, or not at all. ...
... you to identify the following tasks as either possible (with algorithms and data structures introduced in this course), impossible, or an open research problem. You may use each letter once, more than once, or not at all. ...
TECH REPORT
... take O(log log n) time and Store operations take O(log log n) amortized expected time. The algorithm uses linear space. Because any data structure can be implemented with an array, we can use the techniques described in this paper to make any data structure fully persistent at a cost of an extra fac ...
... take O(log log n) time and Store operations take O(log log n) amortized expected time. The algorithm uses linear space. Because any data structure can be implemented with an array, we can use the techniques described in this paper to make any data structure fully persistent at a cost of an extra fac ...
csci 210: Data Structures Maps and Hash Tables
... • Hashing can provide better performance than binary search trees if the keys are sufficiently random so that a good hash function can be developed • when hashing works, better use hashing than BST • However • Hashing does not guarantee worst-case performance • Binary search trees support a wider ra ...
... • Hashing can provide better performance than binary search trees if the keys are sufficiently random so that a good hash function can be developed • when hashing works, better use hashing than BST • However • Hashing does not guarantee worst-case performance • Binary search trees support a wider ra ...
Java Software Solutions
... a root node and potentially many levels of additional nodes that form a hierarchy • Nodes that have no children are called leaf nodes • Nodes except for the root and leaf nodes are called internal nodes • In a general tree, each node can have many child nodes. ...
... a root node and potentially many levels of additional nodes that form a hierarchy • Nodes that have no children are called leaf nodes • Nodes except for the root and leaf nodes are called internal nodes • In a general tree, each node can have many child nodes. ...
Threaded Trees Institute of Lifelong Learning, University of Delhi
... looking at its iterative counterpart we have few questions in our mind like, whether stack can be removed in iterative version. If not, then is iterative version of in order more efficient than the recursive version. Whether there is some another means of doing inorder traversal without using stack ...
... looking at its iterative counterpart we have few questions in our mind like, whether stack can be removed in iterative version. If not, then is iterative version of in order more efficient than the recursive version. Whether there is some another means of doing inorder traversal without using stack ...
DSLec(Hashing). - CSE246DataStructures
... Worst-case: All keys hash to the same bucket. Insert takes O(1), but delete and find take O(N). In linear probing max Load factor <=1 Performance of the hash tables, based on open addressing scheme is very sensitive to the table's load factor. If load factor exceeds 0.7 threshold, table's speed dras ...
... Worst-case: All keys hash to the same bucket. Insert takes O(1), but delete and find take O(N). In linear probing max Load factor <=1 Performance of the hash tables, based on open addressing scheme is very sensitive to the table's load factor. If load factor exceeds 0.7 threshold, table's speed dras ...
LNCS 4051 - Dynamic Interpolation Search Revisited
... This means that the probabilistic analyses of previous dynamic interpolation search data structures are inapplicable to sequences of non-distinct elements, produced by discrete probability distributions with measurable (non-zero) probability of key collisions. This lack of generalization does not ha ...
... This means that the probabilistic analyses of previous dynamic interpolation search data structures are inapplicable to sequences of non-distinct elements, produced by discrete probability distributions with measurable (non-zero) probability of key collisions. This lack of generalization does not ha ...
Binary search tree
In computer science, binary search trees (BST), sometimes called ordered or sorted binary trees, are a particular type of containers: data structures that store ""items"" (such as numbers, names and etc.) in memory. They allow fast lookup, addition and removal of items, and can be used to implement either dynamic sets of items, or lookup tables that allow finding an item by its key (e.g., finding the phone number of a person by name).Binary search trees keep their keys in sorted order, so that lookup and other operations can use the principle of binary search: when looking for a key in a tree (or a place to insert a new key), they traverse the tree from root to leaf, making comparisons to keys stored in the nodes of the tree and deciding, based on the comparison, to continue searching in the left or right subtrees. On average, this means that each comparison allows the operations to skip about half of the tree, so that each lookup, insertion or deletion takes time proportional to the logarithm of the number of items stored in the tree. This is much better than the linear time required to find items by key in an (unsorted) array, but slower than the corresponding operations on hash tables.They are a special case of the more general B-tree with order equal to two.