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.
When the objective function is parallel to a binding constraint (i.e. constraint that is satisfied as an equation 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 Multiple Optimal solution or alternative optima.
Feasible solution is any element of the feasible region of an optimization problem. The feasible region is the set of all possible solutions of an optimization problem.
An optimal solution is one that either minimizes or maximizes the objective function.
feasible solutions are the points which satisfy the constraint and non-negativity of the problem. optimal feasible solution is the best of all available feasible solutions.
Optimum=best. Feasible-all possible.
what is optimal jamming
optimal tendency is the real outcome of inborn talent bestowed by Almighty Creator which is always distinguish person to person.
arousal level
maltose, its products are glucose, the organ it is used in is duodenum, its optimal pH is 6.1-6.8, and its optimal temperature is 35-40 degrees Celsius.
200c to 450c --> mesophile
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
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.
'optimal' means: best possible compromise solution to a problem, when there are several competing considerations, not all of which can be simulataneously maximized.
If you want to explore in detail about 'why MODX development is the optimal solution for business development', check out this article: franciscahughes.wordpress.com/2018/01/01/call-for-cms-productivity-modx-development-is-one-stop-solution/
Solver
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.
Greedy algorithms are only guaranteed to produce locally optimal solutions within a given time frame; they cannot be guaranteed to find globally optimal solutions. However, since the intent is to find a solution that approximates the global solution within a reasonable time frame, in that sense they will always work. If the intent is to find the optimal solution, they will mostly fail.
Heuristic is one of the method of problem solving, and the goal is not to find the optimal solution but rather to find a good solution quickly.