ipop-class              package:kernlab              R Documentation

_C_l_a_s_s "_i_p_o_p"

_D_e_s_c_r_i_p_t_i_o_n:

     The quadratic problem solver class

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form 'new("ipop", ...)'. or
     by calling the 'ipop' function.

_S_l_o_t_s:

     '_p_r_i_m_a_l': Object of class '"vector"' the primal solution of the
          problem

     '_d_u_a_l': Object of class '"numeric"' the dual of the problem

     '_h_o_w': Object of class '"character"' convergence information

_M_e_t_h_o_d_s:

'_p_r_i_m_a_l' Return the primal of the problem

'_d_u_a_l' Return the dual of the problem

'_h_o_w' Return information on convergence

_A_u_t_h_o_r(_s):

     Alexandros Karatzoglou
      alexandros.karatzoglou@ci.tuwien.ac.at

_S_e_e _A_l_s_o:

     'ipop'

_E_x_a_m_p_l_e_s:

     ## solve the Support Vector Machine optimization problem
     data(spam)

     ## sample a scaled part (300 points) of the spam data set
     m <- 300
     set <- sample(1:dim(spam)[1],m)
     x <- scale(as.matrix(spam[,-58]))[set,]
     y <- as.integer(spam[set,58])
     y[y==2] <- -1

     ##set C parameter and kernel
     C <- 5
     rbf <- rbfdot(sigma = 0.1)

     ## create H matrix etc.
     H <- kernelPol(rbf,x,,y)
     c <- matrix(rep(-1,m))
     A <- t(y)
     b <- 0
     l <- matrix(rep(0,m))
     u <- matrix(rep(C,m))
     r <- 0

     sv <- ipop(c,H,A,b,l,u,r)
     primal(sv)
     dual(sv)
     how(sv)

