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N(N+1)(N-1)
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6 |
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| Three Core Elements by which to assess a Data Structure |
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| Speed, Efficiency, Flexibility |
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| Does 2^20 = [2^10]*[2^10]? |
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| At which x does 2^x does an algorithm's processes become non-feasible by modern computing? |
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| Name of log2N algorithm class |
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| Name of N algorithm class |
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| Name of NlogN algorithm class |
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| Name of N^2 algorithm class |
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| Name of N^3 algorithm class |
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| Name of 2^N algorithm class |
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| Name of N! algorithm class |
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| Extreme best algorithm class |
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| Polynomial Time (GOOD/BAD) |
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| Super Polynomial Time (GOOD/BAD) |
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| Big O refers to an algorithm's core growth being _______ than the growth of g |
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| Big Sigma Notation to an algorithm's core growth being _______ than the growth of g |
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| Big Theta notation refers to an algorithm's core growth being _________ than the growth of g |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| We say that T(n) is O(g(n)) if there exists constants c>0 and n0 >= 0 such that T(n) <= c * g(n) for all N >= n0 |
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| Big O complexity of SEARCH in UNSORTED ARRAYS of size N |
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| Big O complexity of INSERT in UNSORTED ARRAYS of size N |
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| Big O complexity of DELETE in UNSORTED ARRAYS of size N |
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| Big O complexity of DELETE in SORTED ARRAYS of size N |
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| Big O complexity of INSERT in SORTED ARRAYS of size N |
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| Big O complexity of SEARCH in SORTED ARRAYS |
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Go through entire array, select smallest value, insert it in to smallest space so far (starts at arr[0] ).
Go through entire array, select second smallest value, swap in to second smallest index arr[1]
Continue until no swaps occur. |
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Definition
Go through entire array, select smallest value, insert it in to smallest space so far (starts at arr[0] ).
Go through entire array, select second smallest value, swap in to second smallest index arr[1]
Continue until no swaps occur. |
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Definition
Go through entire array, select smallest value, insert it in to smallest space so far (starts at arr[0] ).
Go through entire array, select second smallest value, swap in to second smallest index arr[1]
Continue until no swaps occur. |
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Definition
Go through entire array, select smallest value, insert it in to smallest space so far (starts at arr[0] ).
Go through entire array, select second smallest value, swap in to second smallest index arr[1]
Continue until no swaps occur. |
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| Goes through entire array comparing arr[current] to arr[prior] repeatedly until no swaps occur. |
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| Goes through entire array comparing arr[current] to arr[prior] repeatedly until no swaps occur. |
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| Goes through entire array comparing arr[current] to arr[prior] repeatedly until no swaps occur. |
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| Goes through entire array comparing arr[current] to arr[prior] repeatedly until no swaps occur. |
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| Goes through entire array comparing arr[current] to arr[prior] repeatedly until no swaps occur. |
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| Goes through entire array comparing arr[current] to arr[prior] repeatedly until no swaps occur. |
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| Creates subarrays that are already sorted. Takes in arr[subarr+1] and compares it to arr[subarr], then places it in its position within the subarray. Excels at sorting on-the-fly. |
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| Creates subarrays that are already sorted. Takes in arr[subarr+1] and compares it to arr[subarr], then places it in its position within the subarray. Excels at sorting on-the-fly. |
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| Creates subarrays that are already sorted. Takes in arr[subarr+1] and compares it to arr[subarr], then places it in its position within the subarray. Excels at sorting on-the-fly. |
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| Creates subarrays that are already sorted. Takes in arr[subarr+1] and compares it to arr[subarr], then places it in its position within the subarray. Excels at sorting on-the-fly. |
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| Creates subarrays that are already sorted. Takes in arr[subarr+1] and compares it to arr[subarr], then places it in its position within the subarray. Excels at sorting on-the-fly. |
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| Creates subarrays that are already sorted. Takes in arr[subarr+1] and compares it to arr[subarr], then places it in its position within the subarray. Excels at sorting on-the-fly. |
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| Efficiency of SEARCH, DELETE, AND INSERT in STACKS and QUEUES |
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| ABSTRACT DATA TYPES STUDIED |
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| Stacks, Queues, Trees, Graphs |
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| Tree Traversals: In Order Traversal |
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Recurse Left;
Process;
Recurse Right |
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| Tree Traversals: Pre-Order Traversal |
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Process;
Recurse Left;
Recurse Right; |
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| Tree Traversals: Post-Order Traversal |
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Recurse Left;
Recurse Right;
Process; |
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| Tree Traversals: Level-Order |
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Traverse with a stack,
Process N;
Add left child to stack;
Add right child to stack;
Add grandchildren to stack; |
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| A Variable whose type is a class, just a reference, always a fixed size of 32b |
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| a geometric shape with self similarity on infinitely many scales |
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| The complexity relationship of a recursive algorithm. |
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| direct subclasses of exception, usually indicate something that can't be controlled |
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| direct subclasses of runtime exception. Doesn't have to be dealt with. |
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| A non-linear data structure that represents a hierarchical relationship |
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| The "level" at which nodes may be at. Root = Level 0 |
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| Trees: Every child has exactly _____ parent, except root |
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| The descendants of a node are its ____________ |
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| Tree: Subtree rooted at node X consists of X and |
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| all of its descendants including the edges that connect them |
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| Tree: If X has no children, then the X subtree consists of_____ |
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| Tree: A childless node is called a ______ |
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| Tree: A non-leaf node is called an _______ |
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| Tree: An empty tree has no_____ |
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| Trees: Are cycles allowed? |
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| No. A child may not also be a parent, or have a lower level in the tree. |
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| Trees: A binary tree node has at most ________ descendants. |
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| Trees: Perfect binary trees |
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| has as many nodes as possible for its height |
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| Every interior node has two children |
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| Graphs: A pair of vertices in set notation forms an______ |
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| Graphs: Adjacent Vertices are ________ |
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| Graphs: A path is a set of ______ |
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| a path from V to J is a sequence of vertices such that there is an edge from X1 to X1+1 for 1<= i <= k |
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| no repeated vertices except for initial and final vertices |
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| Graphs: The minimum length of a simple cycle is _____ |
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| Graphs: a graph is connected if _______ |
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| there is a path from every vertex to every other vertex |
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| Graphs: a graph is not connected if ______ |
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| there exist different 'pieces' of the graph |
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| Graphs: A graph is complete if _______ |
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| all vertices have all possible edges |
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| Graphs: An adjacency matrix is _______ |
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| sometimes rep'd as a square 2D array, V in length, with 1's and 0's if connections exist. Mirrored about the middle. |
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| Graphs: The degree of a node is ________ |
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| the number of neighbors it has |
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