Algorithm for Alpha-beta pruning using minimax. in: node v; alpha value α; beta value β out: utility value of node v

True or false: In the SVM quadratic optimization, increasing the magnitude of the weight vector serves to maximize the margin of the decision boundary.

Knapsack 0/1 problem: Given N items where each item has some weight and profit associated with it and also given a bag with capacity W, [i.e., the bag can hold at most W weight in it]. The task is to put the items into the bag such that the sum of profits associated with them is the maximum possible. Given the problem is solved using a dynamic programming approach and the matrix derived is given below, answer the below set of questions by analyzing the DP matrix. weights = [2, 3, 4, 5], profits = [1, 2, 5, 6], Capacity W = 8 Capacity 2 3 Profits weights|0 1 2 5 16 14 |-> 5 10 0 O 10 1 2 3 4 0 O 0 1 1 0 1 0 1 O 10 1 2 2 2 5 2 O 15 50 1 356 6 O 1 3 6 18 00378 7 10 10 1 3 7 7 1 18

Consider the following code:> x <- matrix(rnorm(n = 500), ncol = 5)> varx <- var(x)Starting with varx, use two applications of the sweep() function, one dividing each row of the matrix andthe other dividing each column, of a covariance matrix to obtain R, the correlation matrix.

Please solve with Exel

Generate test cases for the triangle problem by using the cause-effect graph and decision table testing

demonstrate that SAT P if P = NP.