def svm_subgradient_solver(x, y, C, tol=1e-6, max_iter=100, verbose=False):
    Dict={}
    w=np.zeros(x.shape[1])
    g=np.zeros(x.shape[1])
    a = 1
    b = 10000
    for iteration in range(max_iter):
        g=np.copy(w)
        for n in range(y.shape[0]):
            if 1>y[n]*np.sum(w*x[n]):
                g-=C*y[n]*x[n]
        w=w-g * a / b
        Sum=0
        for i in range(y.shape[0]):
            Sum+=max(0,1-y[i]*np.sum(w*x[i]))
        print( 1/2*(np.sum(w*w)+C*Sum))
    return Dict