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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
Some disadvantages: Cannot be used for categorical data. Difficult with large number of observations. Not very good if there are many stems with no leaves (you must show bare stems). This can be a problem if there are outliers. Ideally require leaves to be ordered on each stem - hard work. Tricky to use (though not impossible) if decimal stem/leaf are not the optimal solution.
V. N. Fomin has written: 'Optimal filtering' -- subject(s): Mathematical optimization, Filters (Mathematics)
The optimal solution is the best feasible solution
the optimal solution is best of feasible solution.this is as simple as it seems
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.
Yukio Inukai has written: 'Optimal versus partially optimal scaling of polychotomous item' -- subject(s): Mathematical optimization
Both are using Optimal substructure , that is if an optimal solution to the problem contains optimal solutions to the sub-problems
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
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.
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.
Hans Sollacher has written: 'Stoppregeln' -- subject(s): Decision making, Industrial management, Mathematical models, Optimal stopping (Mathematical statistics)
Stefan Hildebrandt has written: 'Mathematics and optimal form' -- subject(s): Mathematics, Form (Philosophy), Nature (Aesthetics), Motion, Calculus of variations