Examples of using Optimization problem in English and their translations into Indonesian
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Colloquial
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Ecclesiastic
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Ecclesiastic
Solving optimization problems.
J is the objective function of the optimization problem.
Solve the optimization problem.
Dynamic programming is typically applied to optimization problems.
Some optimization problem.
Distributed solution to an optimization problem.
The optimization problem for the PU can be formulated as.
Solving the optimization problem.
The problems of maximization and minimization are also called optimization problems.
Solve this optimization problem.
The problem we were dealing here was an optimization problem.
SVM solves the optimization problem as follows.
This algorithm hasbeen applied to search an optimum solution in many optimization problems.
In this case, our optimization problem becomes.
In an optimization problem, the fitness function simply computes the value of the objective function.
Different techniques have been developed to solve optimization problems with more than one goal.
Every optimization problem has a corresponding decision problem. .
The Vehicle Routing Problem(VRP) is a prominent combinatorial optimization problem in distribution planning.
An optimization problem is a computational problem in which the objective is to find the best of all possible solutions.
The maximum hyperplane problem can be formulated as the following Quadratic Programming(QP) optimization problem[21].
Management science, optimization problems, mathematical modeling.
For the case of only one constraint and only two choice variables(as exemplified in Figure 1),consider the optimization problem maximize f(x, y) subject to g(x, y) 0.
The optimization problem for synthesizing an image attempts to jointly preserve the shape of the entire face and appearance of the local fiducial details.
In the 1950s, more well-defined models built on discounted utility theory andapproached the question of inter-temporal consumption as a lifetime income optimization problem.
In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of this function.
The key result in dynamic programming is the Bellman equation,which writes the value of the optimization problem at an earlier time(or earlier step) in terms of its value at a later time or later step.
Genetic algorithms are implemented as a computer simulation in which a population of abstract representations(called chromosomes or the genotype or the genome) of candidate solutions(called individuals, creatures,or phenotypes) to an optimization problem evolves toward better solutions.
In a genetic algorithm, a population of candidate solutions(called individuals, creatures,or phenotypes) to an optimization problem is evolved toward better solutions.
GAs are implemented as a computer simulation in which a population of abstract representations(called chromosomes)of candidate solutions(called individuals) to an optimization problem evolves toward better solutions.