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Linear Programming Test - 6
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Linear Programming Test - 6

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  • Question 1/10
    1 / -0.25

    If the system of constraints has no point which satisfies all the constraints and non-negativity restrictions then the solution of a LPP is an ​

  • Question 2/10
    1 / -0.25

    The optimum value of the objective function is attained at the points ​

    Solutions

    The optimal value of the objective function is attained at the points given by corner points of the feasible region.

  • Question 3/10
    1 / -0.25

    The maximum value of Z = 4x + 3y subjected to the constraints 3x + 2y ≤160, 5x + 2y ≤200, x + 2y ≤80 ;
    x, y ≥0 attains at ​

  • Question 4/10
    1 / -0.25

    A feasible solution of a LPP if it also optimizes the objective function is called ​

  • Question 5/10
    1 / -0.25

    Shape of the feasible region formed by the following constraints is x + y ≤10, x + y ≥6, x ≥0, y ≥0 ​

    Solutions

    x + y ≤10
    x + y = 10  
    For x = 0, y = 10
    x + y ≥6,   x + y = 6
    For x = 0, y =6
    Hence, the image will be the trapezium.

  • Question 6/10
    1 / -0.25

    Region represented by x ≥0, y ≥0 is ​

  • Question 7/10
    1 / -0.25

    Find the maximum value of z = 3x + 4y subject to constraints x + y ≤4 , x ≥0 and y ≥0 ​

  • Question 8/10
    1 / -0.25

    Infeasibility means that the number of solutions to the linear programming models that satisfies all constraints is ​

  • Question 9/10
    1 / -0.25

    The set of all feasible solutions of a LPP is a ____ set.​

    Solutions

    The set of feasible solutions to an LP (feasible region) forms a (possibly unbounded) convex set.

  • Question 10/10
    1 / -0.25

    Which of the following sets are convex ?​

    Solutions

     {(x,y) : y ≥2 , y ≤4} is the region between two parallel lines, so any line segment joining any two points in it lies in it. Hence, it is the convex set.

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