A set of rules to be followed during calculations or other problem solving operations is what defines algorithm. Computational complexity for instance defines clearly the major concern of algorithm, which is efficiency, which involves how much time and memory required to solve problem. Bt0080 - Fundamentals of algorithms will help you understand what algorithm is all about.
Optimal solution
Local solution
Exact solution
Correct solution correct solution correct solution
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Maximum cost, optimality
Maximum cost, generality
Minimum cost, generality
Minimum cost ,optimality
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Definiteness
Finiteness
Correctness
Effectiveness
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Itself, recursive
By other program, recursive
Itself, non-recursive
Some other function , recursive
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Direct sequencing
Indirect sequencing
Direct selection
Indirect selection
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Divide and conquer
Decrease and conquer
Space and time tradeoff
Greedy method
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FIFO
LIFO
LILO
FILO
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Null tree
Rooted tree
Graph
Forest
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Divide and conquer technique
Greedy technique
Decrease and conquer technique
Time and space tradeoff
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Descendent
Identical
Differ
Local
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Undirected graph
Directed graph
Directed as well as undirected graph
Type of tree
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3.34%
2.34%
1%
4.34%
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Self-loop
Open loop
Closed vertex
Open vertex Open vertex Open vertex
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Euler path
Euler line
Euler forest
Euler tree
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Systematically
Logically
Periodically
Non- systematically
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Comparison
Differentiation
Multiplication
Division
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Depth, height
Right branch., left branch
Left branch, right branch.
Right branch, depth
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Sub matrix
Linked matrix
Simple matrix
Both b and c
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FINITENESS , EFFECTIVENESS
Definiteness, effectiveness
Finiteness, definiteness
Correctness, finiteness
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O(n2).
O(2n).
O(log n2).
Nlog n
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Vertices, edges
Node , path
Weight , direction
Vertices , weight
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Sequence of activities
Step-by step procedure
Flow of information
Both b and c
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FIFO
LIFO
FILO
LILO
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A collection of trees is called a forest.
Almost complete binary tree
Binary tree
Parser tree
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Circuits.
Multiple path
Multiple vertices
Forest
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Connected
Isolated
Directed
Pendent
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Tuple size, input size
Fixed tuple size formulation, variable tuple size formulation
Input size, output size
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[1:n]
[2n:n]
[1:2n]
[2:n]
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1736
1730
1836
1730
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Isomorphic, identical
Isomorphic, different
Tree, identical
Tree, different
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Linear complex
1 – complex
One-dimensional complex
Linear complex, or a 1 – complex, or a one-dimensional complex
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X ≤ y, f(x) ≤ f(y)
X ≤ y, f(x) > f(y)
X > y, f(x) > f(y)
X >y, f(x) ≤ f(y)
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Cost function c (.), (r) ≤ c(r) ≤ u(r)
Copy function, (r) ≤ c(r) ≤ u(r)
Cost function c (.), (r) > c(r) > u(r)
Copy function, (r) > c(r) > u(r)
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When the shortest path to the destination vertex is found
When all the vertices in the graph are included to the path
When the vertices together form a cycle
When the minimum spanning tree is constructed
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Shortest tour
Longest tour
Cost calculator
Cost definition
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1. True, 2. true, 3. true
1. true, 2. false, 3.true
1. false, 2. false, 3. false
1. true, 2. true, 3. false
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1. True, 2. true, 3. true
1. true, 2. false, 3.true
1. false, 2. false, 3. false
1. true, 2. true, 3. false
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Lower bound
Upper bound
Space complexity
Average bound
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Adjacency matrix
Adjacency linked list matrix
Both a and b
Stack
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(0, 1)-matrix
Binary matrix
Both a and b
Weight matrix
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A
B
C
D
Matrix A
Array A
Graph A
Tree A
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Sub matrix
Linked matrix
Simple matrix
Both b and c
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Strictly monotonically increasing function
Monotonically decreasing function
Strictly monotonically decreasing function
Mod function
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State space tree
Binary search tree
Syntax tree
Flowchart
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One directed, asymmetric (or) anti-symmetric digraphs.
One directed, symmetric digraphs.
Two directed, symmetric digraphs.
Two directed, asymmetric
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Single cycle, once
Single cycle, twice
Double cycle, once
Double cycle, twice
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1. True, 2. true, 3. true
1. true, 2. false, 3.true
1. false, 2. false, 3. false
1. true, 2. true, 3. false
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