Chapter 17
... // Find the last node in the list. while (nodePtr->next) nodePtr = nodePtr->next; // Insert newNode as the last node. nodePtr->next = newNode; ...
... // Find the last node in the list. while (nodePtr->next) nodePtr = nodePtr->next; // Insert newNode as the last node. nodePtr->next = newNode; ...
Linked Lists
... // Find the last node in the list. while (nodePtr->next) nodePtr = nodePtr->next; // Insert newNode as the last node. nodePtr->next = newNode; ...
... // Find the last node in the list. while (nodePtr->next) nodePtr = nodePtr->next; // Insert newNode as the last node. nodePtr->next = newNode; ...
Stacks Stack Abstract Data Type Stack (supporting methods) Stack
... Two nodes that are children of the same parent are siblings A node is external (leaf) if it has no children, and it is internal otherwise Parent-child relationship naturally extends to ancestor-descendent relationship A tree is ordered if there a linear ordering defined for the children of each node ...
... Two nodes that are children of the same parent are siblings A node is external (leaf) if it has no children, and it is internal otherwise Parent-child relationship naturally extends to ancestor-descendent relationship A tree is ordered if there a linear ordering defined for the children of each node ...
Linked Lists
... // Find the last node in the list. while (nodePtr->next) nodePtr = nodePtr->next; // Insert newNode as the last node. nodePtr->next = newNode; ...
... // Find the last node in the list. while (nodePtr->next) nodePtr = nodePtr->next; // Insert newNode as the last node. nodePtr->next = newNode; ...
Lecture 21
... Proof by contradiction that Kruskal’s finds a minimum spanning tree: • Assume another spanning tree has lower cost than Kruskal’s. • Pick an edge e1 = (u, v) in that tree that’s not in Kruskal’s. • Consider the point in Kruskal’s algorithm where u’s set and v’s set were about to be connected. Kruska ...
... Proof by contradiction that Kruskal’s finds a minimum spanning tree: • Assume another spanning tree has lower cost than Kruskal’s. • Pick an edge e1 = (u, v) in that tree that’s not in Kruskal’s. • Consider the point in Kruskal’s algorithm where u’s set and v’s set were about to be connected. Kruska ...
Linked Lists
... • In a circular linked list with more than one node, it is convenient to make the reference variable first point to the last node of the list ...
... • In a circular linked list with more than one node, it is convenient to make the reference variable first point to the last node of the list ...
Data structures and performance analysis
... A second approach to testing is random testing, in which the inputs are generated randomly but in a way that satisfies the preconditions. A random test case might generate calls to a single randomly chosen method or generate a sequence of randomly chosen method calls against an object of the tested ...
... A second approach to testing is random testing, in which the inputs are generated randomly but in a way that satisfies the preconditions. A random test case might generate calls to a single randomly chosen method or generate a sequence of randomly chosen method calls against an object of the tested ...
Text Processing in Linux A Tutorial for CSE 562/662 (NLP)
... Now I'd like to see that same list, but only see each word once (unique). hint: you can tell 'sort' which fields to sort on e.g., sort +3 –4 will skip the first 3 fields and stop the sort at the end of field 4; this will then sort on the 4th field. sort –k 4,4 will do the same thing for f in out*; d ...
... Now I'd like to see that same list, but only see each word once (unique). hint: you can tell 'sort' which fields to sort on e.g., sort +3 –4 will skip the first 3 fields and stop the sort at the end of field 4; this will then sort on the 4th field. sort –k 4,4 will do the same thing for f in out*; d ...
Hierarchical range queries on encrypted data
... Assume we assigned to each client a maximum precision level on which he is able to send queries to the database manager. Our aim is to build query mechanism that guarantees this property and makes it impossible for the client to send more precise queries. As an additional requirement, we would like ...
... Assume we assigned to each client a maximum precision level on which he is able to send queries to the database manager. Our aim is to build query mechanism that guarantees this property and makes it impossible for the client to send more precise queries. As an additional requirement, we would like ...
CSE 143, Winter 2010 Final Exam Thursday, March 18, 2010
... o (You can make more than one pass over the list, but you may not make k or N passes over it.) Do not call any methods of the linked list class to solve this problem. o (Note that the list does not have a size field, and you are not supposed to call its size method.) Do not use auxiliary data struct ...
... o (You can make more than one pass over the list, but you may not make k or N passes over it.) Do not call any methods of the linked list class to solve this problem. o (Note that the list does not have a size field, and you are not supposed to call its size method.) Do not use auxiliary data struct ...
Elementary Data Structures: Binary Search Trees
... Each of the nodes a and c has at least one child and is an internal node. Prof. Amr Goneid, AUC ...
... Each of the nodes a and c has at least one child and is an internal node. Prof. Amr Goneid, AUC ...
05-linkedlist
... Returns the index in this list of the last occurrence of the specified element, or -1 if the list does not contain it. public Object remove(int index) Removes the object at the specified position in this list. public Object set(int index, Object o) Replaces the element at the specified position in t ...
... Returns the index in this list of the last occurrence of the specified element, or -1 if the list does not contain it. public Object remove(int index) Removes the object at the specified position in this list. public Object set(int index, Object o) Replaces the element at the specified position in t ...
B-tree
In computer science, a B-tree is a tree data structure that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time. The B-tree is a generalization of a binary search tree in that a node can have more than two children (Comer 1979, p. 123). Unlike self-balancing binary search trees, the B-tree is optimized for systems that read and write large blocks of data. B-trees are a good example of a data structure for external memory. It is commonly used in databases and filesystems.