Here is small model that is infeasible: min x^2 + y^2 x+y=1 x>=2 x,y>=0 However when I try this with SLSQP I get a return code that seems to indicate everything is hunky-dory: x = [1,2] optimize.minimize( lambda x: x[0]**2+x[1]**2, x, constraints = ( {'type':'eq','fun': lambda x: x[0]+x[1]-1}, {'type':'ineq','fun': lambda x: x[0]-2} ), bounds = ((0,None),(0,None)), method='SLSQP') nfev: 8 fun: 2.77777777777712 nit: 6 jac: array([ 3.33333334e+00, 2.98023224e-08, 0.00000000e+00]) x: array([ 1.66666667e+00, 1.39888101e-14]) success: True message: 'Optimization terminated successfully.' status: 0 njev: 2 R has a similar interface to SLSQP, so I tried that also. We see that a number of flags are raised. slsqp(c(1,2), function(x) {x[1]^2+x[2]^2}, heq=function(x){x[1]+x[2]-1}, hin=function(x){x[1]-2}, lower=c(0,0)) $par [1] 1.666667e+00 4.773719e-11 $value [1] 2.777778 $iter [1] 105 $convergence [1] -4 $message [1] "NLOPT_ROUNDOFF_LIMITED: Roundoff errors led to a breakdown of the optimization algorithm. In this case, the returned minimum may still be useful. (e.g. this error occurs in NEWUOA if one tries to achieve a tolerance too close to machine precision.)" I am not very familiar with this solver (so I may be doing something wrong or misinterpreting something) but it looks like the scipy version does not return a correct return code. Thanks, Erwin