what is the difference between the tree search and graph search algorithms?
535654
The only difference between a graph and a tree is cycle. A graph may contain cycles, a tree cannot. So when you're going to implement a search algorithm on a tree, you don't need to consider the existence of cycles, but when working with an arbitrary graph, you'll need to consider them.
319418
DFS uses Stack to find the shortest path. BFS is better when target is closer to Source. DFS is better when target is far from source. As BFS considers all neighbour so it is not suitable for decision tree used in puzzle games.
1243243
Data Structure - Depth First Traversal. Depth First Search (DFS) algorithm traverses a graph in a depthward motion and uses a stack to remember to get the next vertex to start a search, when a dead end occurs in any iteration.
1344462
Queue is used to implement traveral in Breadth First Search. Stack is used to implement traversal in Depth First Search.
75874
Tree topology is less expensive than mesh topology. The difference between Mesh Topology and Tree Topology are as follows: ... In Mesh topology, each node is connected to every other node fully through dedicated link. In tree topology, the nodes are organized in the design of a tree.
739403
Binary search of the data structure link lists are not suitable. Binary search based on divide and conquer algorithm, determination of middle element is important. ... A linked list is a linear collection of data elements, whose order is not given by their physical placement in memory.
732500
In search, states are represented as a single entity (which may be quite a complex object, but its internal structure is not used by the search algorithm). Whereas in planning, states have structured representations (collections of properties) that are used by the planning algorithm.
709392
Greedy algorithm is one which finds feasible solution at every stage with the hope of finding optimal solution whereas Dynamic programming is one which break the problems into series of overlapping sub-problems. ... Greedy algorithm take decision in one time whereas Dynamic programming take decision at every stage.
510652
DFS stands for Depth First Search. 2. BFS(Breadth First Search) uses Queue data structure for finding the shortest path. ... BFS can be used to find single source shortest path in an unweighted graph, because in BFS, we reach a vertex with minimum number of edges from a source vertex.
527788
['Linear: arrays, lists.', 'Tree: binary, heaps, space partitioning etc.', 'Hash: distributed hash table, hash tree etc.', 'Graphs: decision, directed, acyclic etc.']
1044447
The main difference between recursive descent parsing and predictive parsing is that recursive descent parsing may or may not require backtracking while predictive parsing does not require any backtracking. Compilation process includes several phases.
1406795
Gradient Boosting vs Random Forest. ... It has been shown that GBM performs better than RF if parameters tuned carefully [1,2]. Gradient Boosting: GBT build trees one at a time, where each new tree helps to correct errors made by previously trained tree.
763537
B+ tree is an extension of the B tree. The difference in B+ tree and B tree is that in B tree the keys and records can be stored as internal as well as leaf nodes whereas in B+ trees, the records are stored as leaf nodes and the keys are stored only in internal nodes.
1415411
Linear search does the sequential access whereas Binary search access data randomly. Time complexity of linear search -O(n) , Binary search has time complexity O(log n).
719956
What is the difference between the binary-search-tree property and the min-heap property? ... If B is a binary search tree, then x is greater than or equal to its left child, and less than or equal to its right child. If B is a min-heap, then x is less than or equal to its two children.
522637
BFS can be used to find the shortest path, with unit weight edges, from a node (origional source) to another. Whereas, DFS can be used to exhaust all the choices because of its nature of going in depth, like discovering the longest path between two nodes in an acyclic graph.
840026
For the input of size n , an algorithm of O(n) will perform steps perportional to n , while another algorithm of O(log(n)) will perform steps roughly log(n) . Clearly log(n) is smaller than n hence algorithm of complexity O(log(n)) is better. Since it will be much faster.