title: how to get scipy.optimize.linprog to keep going after an unsecessful termination
body: <p>we have been tasked with checking a how many times a linear programming problem terminates successfully, however every time it fails it keeps me out of the loop, is there a way i can do the test and still continue with the next iteration?</p>
  
  <pre><code>from scipy.optimize import linprog
  import numpy as np
  
  #  objective function  maximum
  f = [1 , 1 ]
  
  
  result = []
  hit = 0
  miss = 0
  
  for i in range(1000):
  
      norm1 = np.random.normal(-0.1,0.03) #a random value taken from the normal distribution mean=-0.1, 
  std=0.03
      norm2 = np.random.normal(-0.4,1) # a random value taken from the normal distribution mean=-0.4, 
  std=0.1
      #if norm1&lt;0 and norm2&lt;0:
      A = [ [ -0.12 , -0.04 ]  , [ norm1, norm2] ] 
      b = [-600  , -1000 ]
  
      x0_bounds = (None, None)
      x1_bounds = (None, None)
  
      # Find minimum
  
      res = linprog(f, A_ub=A, b_ub=b, bounds=(x0_bounds, x1_bounds ),
                    options={"disp": False})
      if res.success == True:
          hit+=1
          print("The amount to be invested = {:6.0f}" .format(res.fun) , "pounds" )
      else:
          miss+=1
  
  print(hit)
  print(miss)
  </code></pre>