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It is usually the answer in linear programming. The objective of linear programming is to find the optimum solution (maximum or minimum) of an objective function under a number of linear constraints. The constraints should generate a feasible region: a region in which all the constraints are satisfied. The optimal feasible solution is a solution that lies in this region and also optimises the obective function.
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Which of these branches of mathematics to obtain an optimal best solution to a problem that can be stated in a mathematical form?
There are no branches listed to select from!
Why a feasible region is the unshaded region?
i know that a feasible region, is the region which satisfies all the constraints but i don't know exactly why is the unshaded region regarded as a feasible region instead of the shaded region.
What are the characteristics of integer programming problems?
The algorithms to solve an integer programming problem are either through heuristics (such as with ant colony optimization problems), branch and bound methods, or total unimodularity, which is often used in relaxing the integer bounds of the problem (however, this is usually not optimal or even feasible).
What is the value of 6x 5y at point D in the feasible region?
Given definitions, or descriptions at least, of "point D" and "the feasible region",I might have had a shot at answering this one.
What is the definition of the solution of linear inequalities?
Each linear equation is a line that divides the coordinate plane into three regions: one "above" the line, one "below" and the line itself. For a linear inequality, the corresponding equality divides the plane into two, with the line itself belonging to one or the other region depending on the nature of the inequality. A system of linear inequalities may define a polygonal region (a simplex) that satisfies ALL the inequalities. This area, if it exists, is called the feasible region and comprises all possible solutions of the linear inequalities. In linear programming, there will be an objective function which will restrict the feasible region to a vertex or an edge of simplex. There may also be a further constraint - integer programming - where the solution must comprise integers. In this case, the feasible region will comprise all the integer grid-ponits with the simplex.
What is the difference between feasible and optimal solution?
The optimal solution is the best feasible solution
State the difference between a feasible solution basic feasible solution and an optimal solution of a lpp?
the optimal solution is best of feasible solution.this is as simple as it seems
What is the difference between feasible solution and basic feasible solution?
optimal solution is the possible solution that we able to do something and feasible solution is the solution in which we can achieve best way of the solution
Why optimal solution is only at corner point?
feasible region gives a solution but not necessarily optimal . All the values more/better than optimal will lie beyond the feasible .So, there is a good chance that the optimal value will be on a corner point
What is optimal solution?
It is usually the answer in linear programming. The objective of linear programming is to find the optimum solution (maximum or minimum) of an objective function under a number of linear constraints. The constraints should generate a feasible region: a region in which all the constraints are satisfied. The optimal feasible solution is a solution that lies in this region and also optimises the obective function.
Is (-12) a one solution?
A solution is Pareto optimal if there exists no feasible solution for which an improvement in one objective does not lead to a simultaneous degradation in one (or more) of the other objectives. That solution is a nondominated solution.
Non-degenerate basic feasible solution?
A non-degenerate basic feasible solution in linear programming is one where at least one of the basic variables is strictly positive. In contrast to degenerate solutions where basic variables might be zero, non-degenerate solutions can help optimize algorithms as they ensure progress in the search for the optimal solution.
Definition of basic feasible solution?
feasible solution
Is it possible for an linear programming model to have exact two optimal solutions?
Yes, but only if the solution must be integral. There is a segment of a straight line joining the two optimal solutions. Since the two solutions are in the feasible region part of that line must lie inside the convex simplex. Therefore any solution on the straight line joining the two optimal solutions would also be an optimal solution.
Is Feasible region is necessary to be a convex set?
Yes, in optimization problems, the feasible region must be a convex set to ensure that the objective function has a unique optimal solution. This is because convex sets have certain properties that guarantee the existence of a single optimum within the feasible region.
What is the corner point solution method?
The corner point solution method is a technique used in linear programming to find the optimal solution by considering the intersection points of the constraints. It involves analyzing the extreme points or corner points of the feasible region to identify the optimal value of the objective function. This method is effective for problems with few variables and constraints.
MODI method of solving transportation problem?
The first approximation to is always integral and therefore always a feasible solution. Rather than determining a first approximation by a direct application of the simplex method it is more efficient to work with the table given below called the transportation table. The transportation algorithm is the simplex method specialized to the format of table it involves: i) finding an integral basic feasible solution ii) testing the solution for optimality iii) improving the solution, when it is not optimal iv) repeating steps (ii) and (iii) until the optimal solution is obtained.
