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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.
An informal synonym for the term optimal is the word ace. Ideal is also a good synonym, as well as matchless. There are a lot of other synonyms that can be found in a thesaurus or online.
Yes, a linear programming problem can have exactly two optimal solutions. This will be the case as long as only two decision variables are used within the problem.
choice, best, select, perfect, optimum, ace, excellent, solid
Dependence on an Electronic Computer: O.R. techniques try to find out an optimal solution taking into account all the factors. In the modern society, these factors are enormous and expressing them in quantity and establishing relationships among these require voluminous calculations that can only be handled by computers.Non-Quantifiable Factors: O.R. techniques provide a solution only when all the elements related to a problem can be quantified. All relevant variables do not lend themselves to quantification. Factors that cannot be quantified find no place in O.R. models.Distance between Manager and Operations Researcher: O.R. being specialist's job requires a mathematician or a statistician, who might not be aware of the business problems. Similarly, a manager fails to understand the complex working of O.R. Thus, there is a gap between the two.Money and Time Costs: When the basic data are subjected to frequent changes, incorporating them into the O.R. models is a costly affair. Moreover, a fairly good solution at present may be more desirable than a perfect O.R. solution available after sometime.Implementation: Implementation of decisions is a delicate task. It must take into account the complexities of human relations and behaviour.
V. N. Fomin has written: 'Optimal filtering' -- subject(s): Mathematical optimization, Filters (Mathematics)
In mathematical optimization, the keyword "k to epsilon not" represents the convergence rate of an algorithm. It signifies how quickly the algorithm can find the optimal solution as the number of iterations increases. A faster convergence rate, indicated by a smaller value of "k to epsilon not," means the algorithm can reach the optimal solution more efficiently.
The optimal solution is the best feasible solution
To use a constrained optimization calculator to find the optimal solution for your problem, you need to input the objective function you want to maximize or minimize, along with any constraints that limit the possible solutions. The calculator will then use mathematical algorithms to determine the best solution that satisfies the constraints.
An optimization problem is a mathematical problem where the goal is to find the best solution from a set of possible solutions. It can be effectively solved by using mathematical techniques such as linear programming, dynamic programming, or heuristic algorithms. These methods help to systematically search for the optimal solution by considering various constraints and objectives.
the optimal solution is best of feasible solution.this is as simple as it seems
To determine the optimal pH level for a solution, you can use a pH meter or pH strips to measure the acidity or alkalinity of the solution. The optimal pH level will depend on the specific application or desired outcome of the solution. It is important to consider factors such as the properties of the substances in the solution and the intended use of the solution when determining the optimal pH level.
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
rearranging branches to find the most optimal tree topology.
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
Backtracking[1] It is used to find all possible solutions available to the problem.[2] It traverse tree by DFS(Depth First Search).[3] It realizes that it has made a bad choice & undoes the last choice by backing up.[4] It search the state space tree until it found a solution.[5] It involves feasibility function.Branch-and-Bound (BB)[1] It is used to solve optimization problem.[2] It may traverse the tree in any manner, DFS or BFS.[3] It realizes that it already has a better optimal solution that the pre-solution leads to so it abandons that pre-solution.[4] It completely searches the state space tree to get optimal solution.[5] It involves bounding function.http://wiki.answers.com/What_is_Difference_between_backtracking_and_branch_and_bound_method#ixzz1FGb9GEwp
The recommended keyword density of lye solution in content for optimal effectiveness is generally around 1-2.