Module #7 Flashcards

1
Q

is any algorithm that follows the
problem-solving heuristic of making the locally optimal choice at each stage with the intent of finding a global
optimum.

A

greedy algorithm

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2
Q

greedy algorithms have how many components

A

5 components

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3
Q

from which a solution is created

A

Candidate Set

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4
Q

which chooses the best
candidate to be added to the solution

A

Selection Function

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5
Q

that is used to determine if a
candidate can be used to contribute to a solution

A

Feasibility Function

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6
Q

which assigns a value to a
solution, or a partial solution, and

A

Objective Function

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7
Q

which will indicate when we have
discovered a complete solution

A

Solution Function

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8
Q

is a greedy algorithm that finds a minimum spanning tree for a weighted
undirected graph.

A

Prim’s Algorithm

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9
Q

also known as Jarnik’s Algorithm

A

Prim’s Algorithm

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10
Q

The algorithm operates by building this tree one vertex at a time, from
an arbitrary starting vertex, at each step adding the cheapest possible
connection from the tree to another vertex.

A

Prim’s Algorithm

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11
Q

The prim’s algorithm was developed in ____ by Czech mathematician Vojtěch Jarník

A

1930

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12
Q

Prim’s Algorithm was later rediscovered and republished by computer scientists Robert C. Prim in

A

1957

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13
Q

Prim’s Algorithm was later rediscovered and republished by computer scientists Robert C. Prim and Edsger W. Dijkstra in

A

1959

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14
Q

Time Complexity of Prim’s Algorithm

A

O(n^2)

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15
Q

Each iteration of for loop takes

A

O(n) time

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16
Q

is a special kind of tree that minimizes the lengths (or “weights”) of the edges of the tree

A

minimum spanning tree

17
Q

Kruskal’s Algorithm was first described by Kruskal in ______ in the same paper where he rediscovered Jarnik’s algorithm.

A

1956

18
Q

Kruskal’s Algorithm was also rediscovered by Loberman and Weinberger in what year

A

1957

19
Q

is a minimum-spanning-tree algorithm which finds an edge of the least possible weight that connects any two trees in the
forest.

A

Kruskal’s algorithm

20
Q

It is a greedy algorithm in graph theory as it finds a minimum spanning tree for a connected weighted graph adding increasing cost arcs at each step.

A

Kruskal’s Algorithm

21
Q

Time Complexity of Kruskal’s Algorithm

A

O(E log E)

22
Q

for finding the shortest path from
a given node to all other nodes in a graph

A

Dijkstra’s algorithm

23
Q

Always takes the shortest edge connecting a known node to an unknown
node

A

Dijkstra’s algorithm

24
Q

is an algorithm for finding the shortest paths between nodes
in a graph, which may represent, for example, road networks.

A

Dijkstra’s algorithm

25
Q

Dijkstra’s algorithm was conceived by computer scientist Edsger W. Dijkstra in

A

1956

26
Q

The algorithm exists in many variants. Dijkstra’s original algorithm found the shortest path between two given nodes, but a more common variant fixes a single node as the “source” node and finds shortest paths from the source to all other nodes in the graph, producing a shortest-path tree.

A

Dijkstra’s algorithm

27
Q

Edsger Dijkstra invented Dijkstra’s Algorithm in

A

1959

28
Q

He kick-started what became known as the Structured Programming movement.

A

Edsger Dijkstra

29
Q

was a university professor for much of his life, which spanned the era when basic methods of computer programming were still being worked out.

A

Edsger Dijkstra

30
Q

Dijkstra pointed out that this
resulted in programs that were difficult to follow, and therefore very difficult
for other programmers to change the journal of the Association for Computing Machinery entitled Go To Statement Considered Harmful in a letter in

A

1968

31
Q

He invented the Semaphore, the mechanism that allows concurrent programs to share a single resource.

A

Edsger Dijkstra

32
Q

was born in the Netherlands, where he worked as a university researcher and professor until 1984

A

Edsger Dijkstra

33
Q

Edsger Dijkstra took up a professorship at the

A

University of Texas at Austin

34
Q

is an algorithm we can use to find shortest
distances or minimum costs depending on what is represented in a graph.

A

Dijkstra’s algorithm

35
Q

Time Complexity of Dijkstra’s algorithm

A

O(E+V log V)

36
Q
A