title: Working with Pulp in python: variables stalled
body: <p>I'm trying to implement the following LP in Python/Pulp in order to compare to Sklearn:</p>
  
  <p><a href="https://i.stack.imgur.com/xgloq.jpg" rel="nofollow noreferrer">LP for Support Vector Machine</a></p>
  
  <pre><code>    #universe
      from pulp import *
      import numpy as np
      from sklearn.metrics.pairwise import rbf_kernel
  
      #define the function to regresse
      N = 100
      x = np.linspace(0, 2 * np.pi, N)
      y = np.sin(x) + np.random.random(N) * 1
      x.resize(N, 1)
      K = rbf_kernel(x, gamma=5)
  
      # define de problem for Pulp
  
      # define the hyper parameters
      C = 10
      eps = 0.1
  
      # define the variables
      RANGE = list(range(N))
      alpha = pulp.LpVariable.dicts("alpha", (RANGE,), cat='Continuous')
      b = pulp.LpVariable("b", cat='Continuous')
      a = pulp.LpVariable.dicts("a", (RANGE,), lowBound=0, cat='Continuous')
      Eta = pulp.LpVariable.dicts("Eta", (RANGE,), lowBound=0, cat='Continuous')
  
      prob = LpProblem("Constrained SVM", LpMinimize)
  
      # implementing the constrains to Pulp
      for i in range(N):
          prob += lpDot(alpha, K[i]) + b - y[i] &gt;= -Eta[i]
          prob += lpDot(alpha, K[i]) + b - y[i] &lt;= Eta[i]
          prob += eps &gt;= 0 #useless constraint
          prob += eps &lt;= Eta[i]
          prob += alpha[i] &lt;= a[i]
          prob += alpha[i] &gt;= -a[i]
  
      # call the objective function
      prob += lpSum(a) + C * lpSum(Eta)
  
      #output file
      prob.writeLP("MySVM.lp")
  
      #launch solver and read result
      prob.solve()
      print("Status:", LpStatus[prob.status])
  </code></pre>
  
  <p>my issue is the solver doesn't to do anything for <strong>alpha</strong> and <strong>a</strong>. They are equal to zero at the end. Is it due to coding error or my LP is badly set? </p>