Linear Programming (LP) is one of the most scoring and conceptually important topics for Class 12 Mathematics as it directly trains you to model real-life constraints into inequalities and optimize a linear objective. It also has high relevance in competitive exams because the same core ideas—feasible region, corner points, and conditions for unique vs infinitely many optimal solutions—appear repeatedly in different forms and parameter-based questions.
25
Minutes
20
Questions
1 / -0
Marking
Q1. Consider the linear programming problem (LPP):
subject to
.
The maximum value of is:
Q2. Maximize
subject to
.
The maximum value of is:
Q3. Consider the LPP
subject to
.
Which of the following describes the set of optimal solutions?
Unique optimal solution at with
Unique optimal solution at with
No finite maximum since feasible region is unbounded
Infinitely many optimal solutions lying on the segment joining and , each with
Q4. For which value(s) of does the LPP
subject to
have infinitely many optimal solutions?
only
only
or
Q5. Assertion (A): Consider the LPP
subject to
. Then the LPP has a finite maximum.
Reason (R): The constraint implies for every feasible point, so is bounded above by .
Both (A) and (R) are true and (R) is a correct explanation of (A)
Both (A) and (R) are true but (R) is not a correct explanation of (A)
(A) is true but (R) is false
(A) is false but (R) is true
Q6. A linear programming problem is given: maximize subject to
. Find the maximum value of .
Q7. Consider the linear programming problem: minimize subject to
. Determine the minimum value of and where it occurs.
Minimum is , attained only at .
Minimum is , attained at every point on the line segment joining and .
Minimum is , attained at .
No minimum; the problem is unbounded below.
Q8. For the linear programming problem maximize subject to
, for which value(s) of does the maximum value of occur at more than one vertex of the feasible region?
Q9. Statement I: The linear program maximize subject to
has infinitely many optimal solutions.
Statement II: The second constraint is redundant (a multiple of the first) and the objective function is proportional to , so the maximum is attained on the entire line within the feasible region.
Both statements are true and Statement II is a correct explanation of Statement I.
Both statements are true but Statement II is not a correct explanation of Statement I.
Statement I is true and Statement II is false.
Statement I is false and Statement II is true.
Q10. Let the linear programming problem be: maximize subject to
. For which values of is the point the unique optimal solution?
Q11. Maximise subject to . The maximum value of is
at
at
at
at
Q12. For which values of is the point an optimal solution of the LP: maximise subject to ?
Q13. Consider the LP: maximise subject to . Which statement is correct about the optimal solution?
Unique maximum at with
Unique maximum at with
Maximum attained only at with
Infinitely many optimal solutions lie on the line segment joining and , with
Q14. Maximise subject to . Which of the following is true?
Unique maximum at with
Infinitely many optimal solutions along the segment joining and
The LP is unbounded
Unique maximum at with
Q15. For which values of is the point an optimal solution of the LP: maximise subject to ? In addition, when is the optimum at unique?
is optimal only for and not optimal at or
is optimal for , and the optimum is unique for while for or there are infinitely many optimal solutions
is optimal only at and , and unique there
is never an optimal solution
...and 5 more challenging questions available in the interactive simulator.