Приклади вживання Objective function Англійська мовою та їх переклад на Українською
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Equation(8) gives the objective function.
The objective function of tax administration[Tselevaia funktsiia administrirovaniia nalogov].
That is, the objective is to obtain a good player, but without an objective function.
In a linear program, the objective function and the constraints are linear.
Principles of forming of mechanism of development of intellectual activity of personnel as an objective function of management.
The function f is called an objective function, or cost function. .
When the objective function is convex, then any local minimum will also be a global minimum.
The expression to be maximized or minimized is called the objective function(cTx in this case).
Where the objective function f{\displaystyle f} is convex, as is the feasible set C{\displaystyle C}.
The function f, that is maximized, is called an objective function or cost function. .
However, optimizing this objective function would result in a solution that maps all of the vertices to a single vertex in the uv-coordinates.
For instance, linear programming déals with the case that both the objective function and the constraints are linéar.
The objective function of the strategic planning begins with establishing the company's mission, which expresses the philosophy and the meaning of its existence.
Education is the only specialized subsystem of society, the objective function has the same purpose of the society.
When the objective function is differentiable, sub-gradient methods for unconstrained problems use the same search direction as the method of steepest descent.
The growing importance of perceived quality of service(PQoS) as the objective function of network management is analyzed.
This objective function typically contains rules of a particular style, but could be learned using machine learning methods such as Markov models[9].
The inequalities Ax≤ b andx≥ 0 are the constraints which specify a convex polytope over which the objective function is to be optimized.
Minimization of the least squares objective function by optimal choice of weights optimizes accuracy of fit.
Minimizing(2) can berewritten as a constrained optimization problem with a differentiable objective function in the following way.
In this case, there is no such maximum as the objective function is unbounded, so the answer is"infinity" or"undefined".
Originally developed by Naum Z. Shor and others in the 1960s and 1970s,subgradient methods are convergent when applied even to a non-differentiable objective function.
A convex optimization problem is an optimization problem in which the objective function is a convex function and the feasible set is a convex set.
SAT solvers can also be used to solve optimization problems, by performing a series ofcalls to the solvers to do binary search on the objective function.
Every local minimum is a global minimum;the optimal set is convex; if the objective function is strictly convex, then the problem has at most one optimal point.
It follows that if the objective function Z is strictly convex(strictly concave) and if the domain of solutions of the constraint system is not empty and bounded, then the VP problem always has a unique solution.