COLLEGE OF SUBIC MONTESSORI Sta. Isabel Dinalupihan Bataan Name: Maricar C. Canlas Subject: Quantitative Techniques In Business Year: 2nd year Instructor: Bien Malit Section: BSBA 1.) Constraints - A constraint is something that plays the part of a physical, social or financial restriction. It is a derived form of the intransitive verb form constrained. 2.) Objective Function - An equation to be optimized given certain constraints and with variables that need to be minimized or maximized using nonlinear programming techniques. An objective function can be the result of an attempt to express a business goal in mathematical terms for use in decision analysis, operations research or optimization studies. 3.) Solution - a means of solving a problem or dealing with a difficult situation. Linear programming problem has a solution, then it must occur at a vertex, or corner point, of the feasible set, S, associated with the problem. 4.) Feasible Solution - to a linear program is a solution that satisfies all constraints. A feasible solution to a linear programming problem a. must be a corner point of the feasible region. b. must satisfy all of the problems constraints simultaneously. c. need not satisfy all of the constraints, only the non-negativity constraints. d. must give the maximum possible profit. e. must give the minimum possible cost. 5.) Optimal Solution - to a linear program is a feasible solution with the largest objective function value (for a maximization problem). 6.) Non negative Constraints - a restriction in a linear programming problem stating that negative values for physical quantities cannot exist in a solution. 7.) Mathematical Model - description of a system using mathematicalconcepts and language. The process of developing a mathematical model is termed mathematical modeling. 8.) Linear Program - special case of mathematical programming mathematical optimization. Linear programming is the process of taking various linear inequalities relating to some situation, and finding the best value obtainable under those conditions. A typical example would be taking the limitations of materials and labor, and then determining the best production levels for maximal profits under those conditions. In real life, linear programming is part of a very important area of mathematics called optimization techniques. 9.) Constraint Function - The extent to which a region of DNA is intolerant of mutation, due to a reduction in its ability to carry out the function encoded. 10.) Linear Equations or Function - functions that have x as the input variable, and x has an exponent of only 1 11.) Decision Variable – quantity that the decision-maker controls and added to an optimization problem by instantiating a decision variable and adding the instance to the optimization 12.) Slack Variable – variable that is added to an inequality constraint to transform it to an equality. Introducing a slack variable replaces an inequality constraint with an equality constraint and a nonnegativity constraint. 13.) Surplus Variables – difference between the total value of the true (decision) variables and the number (usually, total resource available) on the right-hand side of the equation. Thus, a surplus variable will always have a negative value. 14.) Feasible Region – set of all possible points (sets of values of the choice variables) of an optimization problem that satisfy the problems constraints, potentially including inequalities, equalities, and integer constraints. 15.) Standard Form – depends on what you are dealing with and convenient way of writing very large or very small numbers. It is used on a scientific calculator when a number is too large or too small to be displayed on the screen. 16.) Redundant Constraint – an inequality or equation of a mathematical programming problem which does not define the solution space. 17.) Extreme Point – simply a point in a convex set C that cannot be expressed as a strict convex combination of any other pair of points in C. Extreme points must be located in specific locations in convex sets. 18.) Alternative Optima – It refers to the distinct solutions to the same optimization problem. When the objective function is parallel to a binding constraint (a constraint that is satisfied in the equality sense by the optimal solution), the objective function will assume the same optimal value at more than one solution point. For this reason they are called alternative optima. 19.) Infeasibility – the quality of not being doable. A linear program is infeasible if there exists no solution that satisfies all of the constraints -- in other words, if no feasible solution can be constructed. Since any real operation that you are modelling must remain within the constraints of reality, infeasibility most often indicates an error of some kind. Simplex-based LP software like lp_solve efficiently detects when no feasible solution is possible. 20.) Unboundedness – A linear is unbounded if the optimal solution is unbounded, i.e. it is either ∞ or −∞. Note that the feasible region may be unbounded, but this is not the same as the linear program being unbounded.
Posted on: Thu, 04 Sep 2014 09:26:36 +0000