A Furniture Dealer Deals In Only Two Items Namely Tables And Chairs. He Has Rs 5000 To Invest And Space To Store Atmost 60 Pieces. A Table Cost Him Rs 250 And A Chair Rs 60. He Can Sell A Table At A
3 Years Ago
Contribute & Test Your Skills
- Sets, Relations & Functions
- Complex Numbers & Quadratic Equations
- Matrices & Determinants
- Mathematical Reasoning
- Mathematical Induction
- Permutations & Combinations
- Sequences & Series
- Continuity
- Integral Calculus
- Differential Equations
- Binomial Theorem
- Co-Ordinate Geometry
- Vector Algebra
- Three Dimensional Geometry
- Statistics & Probability
- Trigonometry
- Differentiability
- Functions
- Limits
- Differentiation
- Application of Derivative
- Difinite Integrals
- Area Under The Curve
- Indices & Surds
- Partial Fractions
- Quadratic Equation
- Exponential Logarithmic Series
- Binary Operations
- Computing
- Regular Cartesian Coordinate
- Straight Lines
- Ellipse
- Hyperbola
- Parabola
- Height & Distances
- Inverse Trigonometrical Functions
- Hyperbolic Functions
- Correlation Regression
- Measure of Central Tendency
- Statistics
- Numerical Methods
- Linear Programming
Popular Questions
- Consider the linear programming problem Maximise
- Which of the following sets are not convex?
- The linear programming problem Maximise
- Which of the term is not used in a linear programming problem?
- The optimal value of the objective function is attained at the point is
- Shaded region is represented by
- A vertex of a feasible region by the linear constraints 3x + 4y
- The maximum value of z is where, z = 4x + 2y subject to constraints
- The area of the feasible region for the following constraints
- The minimum value of the objective function z = 2x + 10y subject to constraints
- Variables of the objective function of the linear programming problem are
- The region represented by the inequation system
- Maximum value of z =12x + 3y, subject to constraints
- The point which provides the solution of the linear programming problem, maximise
- Maximum value of z = 3x + 4y subject to constraints x - y
- The maximum value of z = 4x + 2y subject to constraints
- The maximum value of z = 10x + 6y, subject to constraints
- For the LPP, minimise z = x1 + x2 such that inequalities 5x1 + 10x2
- The maximum value of z = 5x + 3y, subject to constraints 3x + 5y
- If given constraints are 5x + 4y - > 2 , x < - 6 and y < - 7, then the maximum value of the function z = x + 2y is
- The constraints -x1 + x2 < - 1, -x1 + 3x2 < - 9 and x1, x2 < - 0 defines on
- If x + y < - 2 ; x > - 0, y > - 0 is the point at which maximum value of 3x + 2y attained will be
- For an LPP, minimise z = 2x + y subject to constraints 5x + 10y
