D Question 1 The main difference between LP and NLP problems is that NLPs will have a: O linear objective function and nonlinear or linear constraints. O minimum of one nonlinear constraint or a nonlinear objective function. O multilevel objective functions. O multilevel objective function and nonlinear constraints.D Question 2 2 pts In NLP, a local optimum is best described as: O an interior point. O a corner point. O a point yielding no improving direction. O a good starting point for subsequent searches.D Question 3 NLP problems which have slack in all the constraints: O are infeasible. O are only at local optimal solutions. O have zeros for all shadow prices. O are at an interior point of the feasible region.Question 4 In solving the NLP problem, Solver found a degenerate model. This means that: O Solver is cycling and redundant constraints must be removed. O A global optimal solution was found. O The objective function changed very slowly for the last few iterations. O A local optimum was found.D Question 5 A nonzero reduced gradient value indicates the approximate impact: O On the objective function of very small changes in the value of a given variable. O On the RHS of a constraint of a unitary change in the objective function value. O On the objective function of very large changes in the value of a given variable. O On the objective function of unitary change in the RHS of associated constraint