w = Variable(numFeat,numClasses)
    constraints= []
    eta=Variable(numClasses*ki,1)

    obj = Minimize(.5*(matrix_frac(w[0:numFeat:1,0], P)+matrix_frac(w[0:numFeat:1,1], P)+matrix_frac(w[0:numFeat:1,2], P)+matrix_frac(w[0:numFeat:1,3], P))+.5*sum(eta))

    for I in range(numClasses*ki):
        for K in range(numClasses):

            constraints.append(w[0:numFeat:1,TrainClasses[I]-1].T*TrainFeat[I,0:numFeat:1] - w[0:numFeat:1,K].T*TrainFeat[I,0:numFeat:1] >=1- kd[TrainClasses[I]-1:TrainClasses[I]:1,K]-eta[I])
    prob=Problem(obj,constraints)
    prob.solve(solver=cvx.SCS)