title: Why solver ECOS in cvxpy cannot solve this LP problem?
body: <p>I am trying to solve the following LP using ECOS in CVXPY. But it tells me
  '''
  Solver 'ECOS' failed. Try another solver, or solve with verbose=True for more information.
  '''
  When I tried &quot;verbose=True&quot;, it shows:</p>
  <p>'''
  Unreliable search direction detected, recovering best iterate (0) and stopping.</p>
  <p>NUMERICAL PROBLEMS (reached feastol=1.7e+00, reltol=-nan(ind), abstol=3.9e+02).
  Runtime: 0.663398 seconds.
  '''
  Here is my code:
  '''</p>
  <pre><code>cons = np.array([[   1,    0,    0,    0,    0,    0,    0],\
   [  -1,   -0,   -0,   -0,   -0,   -0,   -0],\
   [-160,    1,    0,    0,    0,    0,    0],\
   [  40,   -1,    0,    0,    0,    0,    0],\
   [-160,    0,    1,    0,    0,    0,    0],\
   [  40,    0,   -1,    0,    0,    0,    0],\
   [-160,    0,    0,    1,    0,    0,    0],\
   [  40,    0,    0,   -1,    0,    0,   0],\
   [-160,    0,    0,    0,    1,    0,    0],\
   [  40,    0,    0,    0,   -1,    0,    0],\
   [-160,    0,    0,    0,    0,    1,    0],\
   [  40,    0,    0,    0,    0,   -1,    0],\
   [-160,    0,    0,    0,    0,    0,    1],\
   [  40,    0,    0,    0,    0,    0,   -1],\
   [ -40,    1,    0,    0,    0,    0,    0],\
   [ -55,   -1,    0,    0,    0,    0,    0],\
   [ -40,   -1,    1,    0,    0,    0,    0],\
   [ -55,    1,   -1,    0,    0,    0,    0],\
   [ -40,    0,   -1,    1,    0,    0,    0],\
   [ -55,    0,    1,   -1,    0,    0,    0],\
   [ -40,    0,    0,   -1,    1,    0,    0],\
   [ -55,    0,    0,    1,   -1,    0,    0],\
   [ -40,    0,    0,    0,   -1,    1,    0],\
   [ -55,    0,    0,    0,    1,   -1,    0],\
   [ -40,    0,    0,    0,    0,   -1,    1],\
   [ -55,    0,    0,    0,    0,    1,   -1]])
  
  const = np.array([1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0])
  
  x = cvx.Variable(7)
  
  temp = [0,3,12,14,16,19]
  
  buji = list(set(list(range(26)))-set(temp))
  
  constraints = [cons[temp]@x==const[temp], cons[buji]@x&lt;=const[buji]]
  
  obj = cvx.Minimize(1)
  
  prob = cvx.Problem(obj, constraints)
  
  prob.solve(verbose=True)
  
  print(prob.status)
  </code></pre>
  <p>'''</p>
  <p>What is wrong with this LP problem?</p>