SVM Dual Objective Value

Problem

The dual objective of a hard-margin SVM is sum_i alpha_i - 0.5 * sum_ij alpha_i alpha_j y_i y_j (x_i dot x_j).

Given the feature vectors, the labels, and the dual coefficients, return the objective value.

Hint

Sum the coefficients, then subtract half the quadratic form.

case 1

in: [[[1,0],[0,1]],[1,-1],[1,1]]

expected: 1

case 2

in: [[[1,1],[2,2]],[1,1],[0.5,0.5]]

expected: -1.25

case 3

in: [[[1,0],[-1,0]],[1,-1],[1,1]]

expected: 0

Starter codePython

def svm_dual_objective_value(xs, ys, alpha):
    # Your code here
    pass

3 test cases · run your code against them in the editor