Data Structures and Other Objects Using C++
... The set of operations/methods define the interface to ...
... The set of operations/methods define the interface to ...
Data Structures, Lists, and Trees
... • I.e., need to search through the data structure to see if it contains an item with the value we want ...
... • I.e., need to search through the data structure to see if it contains an item with the value we want ...
R-Trees
... R*-tree: change the insertion, deletion algorithms (minimize not only area but also perimeter, forced re-insertion ) Hilbert R-tree: use the Hilbert values to insert objects into the tree ...
... R*-tree: change the insertion, deletion algorithms (minimize not only area but also perimeter, forced re-insertion ) Hilbert R-tree: use the Hilbert values to insert objects into the tree ...
Fully-online Construction of Suffix Trees for Multiple Texts
... technique to amortize the cost to track the suffix insertion points does not work in our case. (C) Ukkonen’s “open edge” technique to maintain the leaves does not work in our case, either. In Section 5 we will explain in more details why and how these problems arise in our fully-online setting. In t ...
... technique to amortize the cost to track the suffix insertion points does not work in our case. (C) Ukkonen’s “open edge” technique to maintain the leaves does not work in our case, either. In Section 5 we will explain in more details why and how these problems arise in our fully-online setting. In t ...
lec2
... – O(log b N ) rebalancing operations after update – Ω(w(v)) updates below v between consecutive operations on v • Weight-balanced B-tree with branching parameter Bc and leaf parameter B – Updates in O(log B N ) and queries in O(log B N T B) I/Os • Construction bottom-up in O( NB log M B Lars Arge ...
... – O(log b N ) rebalancing operations after update – Ω(w(v)) updates below v between consecutive operations on v • Weight-balanced B-tree with branching parameter Bc and leaf parameter B – Updates in O(log B N ) and queries in O(log B N T B) I/Os • Construction bottom-up in O( NB log M B Lars Arge ...
Chapter 17
... 2. Examine the linkedListIterator class definition, UML diagram, and implementation in detail. Note the use of operator overloading, the this pointer, and the private node pointer. 3. Next, examine the class definition and UML diagram of the abstract class linkedListType. Explain why the copy constr ...
... 2. Examine the linkedListIterator class definition, UML diagram, and implementation in detail. Note the use of operator overloading, the this pointer, and the private node pointer. 3. Next, examine the class definition and UML diagram of the abstract class linkedListType. Explain why the copy constr ...
chapter15
... – calls Node destructor and deallocates memory allocated with new – delete newPtr; • newPtr is not deleted; the space newPtr points to is deallocated 2000 Deitel & Associates, Inc. All rights reserved. ...
... – calls Node destructor and deallocates memory allocated with new – delete newPtr; • newPtr is not deleted; the space newPtr points to is deallocated 2000 Deitel & Associates, Inc. All rights reserved. ...
Vector
... partially full array, but space has been allocated for the full size If one more value needs to be added past the maximum size the array needs to be ...
... partially full array, but space has been allocated for the full size If one more value needs to be added past the maximum size the array needs to be ...
CSE 143 Midterm
... #2: Merge sort achieves an O(N log2 N) runtime by dividing the array in half at each step and then recursively sorting and merging the halves back together. #3: Merge sort runs faster than selection sort because it is recursive, and recursion is faster than for loops. #4: Selection sort runs in O(N) ...
... #2: Merge sort achieves an O(N log2 N) runtime by dividing the array in half at each step and then recursively sorting and merging the halves back together. #3: Merge sort runs faster than selection sort because it is recursive, and recursion is faster than for loops. #4: Selection sort runs in O(N) ...
Chapter 12 Greedy Algorithms for Minimum Spanning Trees
... (A) Any union operation involves at most 2 of the original one-element sets; thus at least n − 2k elements have never been involved in a union (B) Also, maximum size of any set (after k unions) is 2k (C) union(A,B) takes O(|A|) time where |A| ≤ |B|. (D) Charge each element in A constant time to pay ...
... (A) Any union operation involves at most 2 of the original one-element sets; thus at least n − 2k elements have never been involved in a union (B) Also, maximum size of any set (after k unions) is 2k (C) union(A,B) takes O(|A|) time where |A| ≤ |B|. (D) Charge each element in A constant time to pay ...
In-memory hash tables for accumulating text vocabularies
... which uses two multiplications and a modulo for each character in the string. Much greater efficiency, and equally good behaviour, can be obtained from “bitwise” hash functions based on operations such as shift and exclusive-or [5]. 2.4. Other structures Further candidate structures for this task in ...
... which uses two multiplications and a modulo for each character in the string. Much greater efficiency, and equally good behaviour, can be obtained from “bitwise” hash functions based on operations such as shift and exclusive-or [5]. 2.4. Other structures Further candidate structures for this task in ...
Open Data Structures (in C++)
... There are plenty of books that teach introductory data structures. Some of them are very good. Most of them cost money, and the vast majority of computer science undergraduate students will shell out at least some cash on a data structures book. Several free data structures books are available onlin ...
... There are plenty of books that teach introductory data structures. Some of them are very good. Most of them cost money, and the vast majority of computer science undergraduate students will shell out at least some cash on a data structures book. Several free data structures books are available onlin ...
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.