Answer:
B. The cost of tour t is at most twice the cost of the optimal tour.
Explanation:
You are using a polynomial time 2-approximation algorithm to find a tour t for the traveling salesman problem.
The cost of tour t is at most twice the cost of the optimal tour
The equation represented as Cost(t) <= 2 Cost(T)
Where
Cost (t) represents cost of tour t
Cost(T) represents cost of the optimal tour
Answer:
Given that:
A= 40n^2
B = 2n^3
By given scenario:
40n^2=2n^3
dividing both sides by 2
20n^2=n^3
dividing both sides by n^2 we get
20 = n
Now putting n=20 in algorithms A and B:
A=40n^2
= 40 (20)^2
= 40 * (400)
A= 16000
B= 2n^3
= 2 (20)^3
= 2(8000)
B= 16000
Now as A and B got same on n = 20, then as given:
n0 <20 for n =20
Let us take n0 = 19, it will prove A is better than B.
We can also match the respective graphs of algorithms of A and B to see which one leads and which one lags, before they cross at n= 20.
The correct answer is
A. the incorporation of technology into objects we use regularly
#Platogang
Complete Question:
A local area network is:
Answer:
b. a group of personal computers or terminals located in the same general area and connected by a common cable (communication circuit) so they can exchange information such as a set of rooms, a single building, or a set of well-connected buildings.
Explanation:
A local area network (LAN) refers to a group of personal computers (PCs) or terminals that are located within the same general area and connected by a common network cable (communication circuit), so that they can exchange information from one node of the network to another. A local area network (LAN) is typically used in small or limited areas such as a set of rooms, a single building, school, hospital, or a set of well-connected buildings.
Generally, some of the network devices or equipments used in a local area network (LAN) are an access point, personal computers, a switch, a router, printer, etc.
Answer:
b
Explanation:
First, we need to initialize the classifier.
Then, we are required to train the classifier.
The next step is to predict the target.
And finally, we need to evaluate the classifier model.
You will find different algorithms for solving the classification problem. Some of them are like decision tree classification etc.
However, you need to know how these classifier works. And its explained before:
You need to initialize the classifier at first.
All kinds of classifiers in the scikit-learn make use of the method fit(x,y) for fitting the model or the training for the given training set in level y.
The predict(x) returns the y which is the predicted label.And this is prediction.
For evaluating the classifier model- the score(x,y) gives back the certain score for a mentioned test data x as well as the test label y.
