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CS-6515 Algorithms latest already passed
CS-6515 Algorithms latest already passed
[Show more]CS-6515 Algorithms latest already passed
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Add to cartCS6515 Midterm Review questions and answers
DFS outputs - ANSWER-pre array, post array, ccnum array, prev array 
BFS outputs - ANSWER-dist array, prev array 
Explore outputs - ANSWER-visited array. 
When to use Dijkstra's? - ANSWER-Works for both directed and undirected 
graphs. Must have only non-negative edge weights. 
Dijkstra's outputs ...
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Add to cartDFS outputs - ANSWER-pre array, post array, ccnum array, prev array 
BFS outputs - ANSWER-dist array, prev array 
Explore outputs - ANSWER-visited array. 
When to use Dijkstra's? - ANSWER-Works for both directed and undirected 
graphs. Must have only non-negative edge weights. 
Dijkstra's outputs ...
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Add to cartCS6515 Exam 3 questions and answers
CS6515 Exam 3 Study Guide questions and answers
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Add to cartCS6515 Exam 3 Study Guide questions and answers
Weak Duality Theorem 
Feasible x <= Feasible y where c^(zT) x <= b^(T) y. Here c^(T) means transpose and 
same for b^(T). 
Weak Duality Theorem Corollary 1 
If Feasible x = Feasible y, they are optimums c^(T) x^(asterisk) = b^(T) y^(asterisk) . 
Weak Duality Theorem Corollary 2 
If Primal/Dual...
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Add to cartWeak Duality Theorem 
Feasible x <= Feasible y where c^(zT) x <= b^(T) y. Here c^(T) means transpose and 
same for b^(T). 
Weak Duality Theorem Corollary 1 
If Feasible x = Feasible y, they are optimums c^(T) x^(asterisk) = b^(T) y^(asterisk) . 
Weak Duality Theorem Corollary 2 
If Primal/Dual...
Basic Properties of Trees - ANSWER-Tree's are undirected, connected and 
acyclic that connect all nodes. 
1. Tree on n vertices has (n-1) edges -> would have a cycle otherwise (more than 
n-1 edges means cycle) 
2. In tree exactly one path between every pair of vertices (otherwise it's not 
con...
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Add to cartBasic Properties of Trees - ANSWER-Tree's are undirected, connected and 
acyclic that connect all nodes. 
1. Tree on n vertices has (n-1) edges -> would have a cycle otherwise (more than 
n-1 edges means cycle) 
2. In tree exactly one path between every pair of vertices (otherwise it's not 
con...
CS6515 Exam 2 Questions and answers 
If graph G has more than |V | − 1 edges, and there is a unique heaviest edge, 
then this edge cannot be part of a minimum spanning tree - ANSWER-False, 
because the unique heaviest edge may not be part of a cycle 
If G has a cycle with a unique heaviest edge e,...
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Add to cartCS6515 Exam 2 Questions and answers 
If graph G has more than |V | − 1 edges, and there is a unique heaviest edge, 
then this edge cannot be part of a minimum spanning tree - ANSWER-False, 
because the unique heaviest edge may not be part of a cycle 
If G has a cycle with a unique heaviest edge e,...
Knapsack without repetition - ANSWER-k(0) = 0 
for w = 1 to W: 
if w_j >w: k(w,j) = k(w, j - 1) 
else: K(w,j) = max{K(w, j -1),K(w - w_j, j -1) + v_i} 
knapsack with repetition - ANSWER-knapsack repeat(w_i....w_n, w_i... w_n, B) 
k(0) = 0 
for i = 1 to n 
if w_i <= b & k(b) <v_i + K(b-w_i) ...
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Add to cartKnapsack without repetition - ANSWER-k(0) = 0 
for w = 1 to W: 
if w_j >w: k(w,j) = k(w, j - 1) 
else: K(w,j) = max{K(w, j -1),K(w - w_j, j -1) + v_i} 
knapsack with repetition - ANSWER-knapsack repeat(w_i....w_n, w_i... w_n, B) 
k(0) = 0 
for i = 1 to n 
if w_i <= b & k(b) <v_i + K(b-w_i) ...
In a DAG, what makes a pair of vertices strongly connected? - answer-There is a 
path `V→W` and `W→V` 
Conservation of flow - answer-The flow into a vertex V must me equal to the flow 
out of the vertex V 
What problems are in the class NP-Hard? - answer-Any problem to which any 
problem in NP c...
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Add to cartIn a DAG, what makes a pair of vertices strongly connected? - answer-There is a 
path `V→W` and `W→V` 
Conservation of flow - answer-The flow into a vertex V must me equal to the flow 
out of the vertex V 
What problems are in the class NP-Hard? - answer-Any problem to which any 
problem in NP c...
Traversing, reversing, copying, or otherwise working on the full graph running 
time - ANSWER-O(n+m) 
Checking, reading, or removing one vertex running time - ANSWER-O(1) 
Iterating, checking, reading, removing, or otherwise working on all vertices 
running time - ANSWER-O(n) 
Checking, reading, or ...
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Add to cartTraversing, reversing, copying, or otherwise working on the full graph running 
time - ANSWER-O(n+m) 
Checking, reading, or removing one vertex running time - ANSWER-O(1) 
Iterating, checking, reading, removing, or otherwise working on all vertices 
running time - ANSWER-O(n) 
Checking, reading, or ...
Equivalence - ANSWER-"x ≡ y (mod N) means that x/N and y/N have the same 
remainder 
a ≡ b (mod N) and c ≡ d (mod N) then: 
a + c ≡ a + d ≡ b + c ≡ b + d (mod N) 
a - c ≡ a - d ≡ b - c ≡ b - d (mod N) 
a ** c ≡ a ** d ≡ b ** c ≡ b ** d (mod N) 
ka ≡ kb (mod N) for any inte...
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Add to cartEquivalence - ANSWER-"x ≡ y (mod N) means that x/N and y/N have the same 
remainder 
a ≡ b (mod N) and c ≡ d (mod N) then: 
a + c ≡ a + d ≡ b + c ≡ b + d (mod N) 
a - c ≡ a - d ≡ b - c ≡ b - d (mod N) 
a ** c ≡ a ** d ≡ b ** c ≡ b ** d (mod N) 
ka ≡ kb (mod N) for any inte...
What is the running time for Longest Increasing Subsequence (LIS) - 
ANSWER-O(n^2) 
What is the recurrence for Longest Increasing Subsequence (LIS)? - 
ANSWER-L(i) = 1 + max{ L(j) | xj < xi} 
This reads as the answer to index I is 1 + the maximum over all j's between 1 
and i where xj is less th...
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Add to cartWhat is the running time for Longest Increasing Subsequence (LIS) - 
ANSWER-O(n^2) 
What is the recurrence for Longest Increasing Subsequence (LIS)? - 
ANSWER-L(i) = 1 + max{ L(j) | xj < xi} 
This reads as the answer to index I is 1 + the maximum over all j's between 1 
and i where xj is less th...
How do you tell if a graph has negative edges? - ANSWER-when fitting graph on 
a table, if the number of moves decreases the w() from edge to edge, then there 
is a negative edge; 
check from 1 to n 
Why are all pairs Dist(y,z) n^2? - ANSWER-Because it builds a two dim table! 
What is the run time o...
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Add to cartHow do you tell if a graph has negative edges? - ANSWER-when fitting graph on 
a table, if the number of moves decreases the w() from edge to edge, then there 
is a negative edge; 
check from 1 to n 
Why are all pairs Dist(y,z) n^2? - ANSWER-Because it builds a two dim table! 
What is the run time o...
Search Problem - ANSWER-A search problem is specified by an algorithm C that 
takes two inputs, an instance I and a proposed solution S, and runs in time 
polynomial in |I|. We say S is a solution to I if and only if C(I, S) = true 
Steps for an NP Proof - ANSWER-1. Demonstrate that problem B is in ...
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Add to cartSearch Problem - ANSWER-A search problem is specified by an algorithm C that 
takes two inputs, an instance I and a proposed solution S, and runs in time 
polynomial in |I|. We say S is a solution to I if and only if C(I, S) = true 
Steps for an NP Proof - ANSWER-1. Demonstrate that problem B is in ...
Dynamic Programming Purpose - ANSWER-Used for optimization problems 
A set of choices must be made to get an optimal solution 
Find a solution with the optimal value (minimum or maximum) 
Dynamic Programming Applicability - ANSWER-Subproblems are not independent 
A divide-and-conquer approach would ...
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Add to cartDynamic Programming Purpose - ANSWER-Used for optimization problems 
A set of choices must be made to get an optimal solution 
Find a solution with the optimal value (minimum or maximum) 
Dynamic Programming Applicability - ANSWER-Subproblems are not independent 
A divide-and-conquer approach would ...
Computer Science 201: Data Structures & 
Algorithms Ch. 15
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Add to cartComputer Science 201: Data Structures & 
Algorithms Ch. 15
Dijkstra's algorithm 
O((n+m)log(n)). Used to find the shortest distance from one node to every other 
node in a graph. 
Inputs: 
- DAG G=(V,E) with edge weights 
- Source vertex s 
Output: 
- Array dist[...] that tells us the length of the shortest path from s to each other vertex. 
e.g. dist[v] g...
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Add to cartDijkstra's algorithm 
O((n+m)log(n)). Used to find the shortest distance from one node to every other 
node in a graph. 
Inputs: 
- DAG G=(V,E) with edge weights 
- Source vertex s 
Output: 
- Array dist[...] that tells us the length of the shortest path from s to each other vertex. 
e.g. dist[v] g...
Steps to solve a Dynamic Programming Problem - ANSWER-1. Define the Input 
and Output. 
2. Define entries in table, i.e. T(i) or T(i, j) is... 
3. Define a Recurrence relationship - Based on a subproblem to the main 
problem. (hint: use a prefix of the original input 1 < i < n). 
4. Define the...
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Add to cartSteps to solve a Dynamic Programming Problem - ANSWER-1. Define the Input 
and Output. 
2. Define entries in table, i.e. T(i) or T(i, j) is... 
3. Define a Recurrence relationship - Based on a subproblem to the main 
problem. (hint: use a prefix of the original input 1 < i < n). 
4. Define the...
Given the following list, 
my_list = [ 
[0, 1, 2], 
[3, 4, 5], 
[6, 7, 8], 
[9, 10, 11] 
] 
what will be printed when the following line of code is called? 
print(my_list[3][1:]) - ANSWER-[10, 11] 
Given the following list, 
my_list = [ 
[0, 1, 2], 
[3, 4, 5], 
[6, 7, 8], 
[9, 10, 11] 
] 
Which line...
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Add to cartGiven the following list, 
my_list = [ 
[0, 1, 2], 
[3, 4, 5], 
[6, 7, 8], 
[9, 10, 11] 
] 
what will be printed when the following line of code is called? 
print(my_list[3][1:]) - ANSWER-[10, 11] 
Given the following list, 
my_list = [ 
[0, 1, 2], 
[3, 4, 5], 
[6, 7, 8], 
[9, 10, 11] 
] 
Which line...
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