Examples of using Optimization problems in English and their translations into Hungarian
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Many optimization problems fall.
There are minimal optimization problems.
Many optimization problems do not fit the"standard" search mode1 introduced in FUNCTION Section 4.3.
Solving complex global optimization problems.
Combinatorial optimization problems such as parsing and the knapsack problem. .
It can be used to solve some optimization problems.
Several other optimization problems can also be solved more efficiently using algorithms specifically designed for distance-hereditary graphs.
Maßberg, Jens(2015): Geometrical and combinatorial optimization problems.
Management science, optimization problems, mathematical modeling.
So far we have seen several algorithms to solve various optimization problems.
Some other optimization problems that are NP-complete on more general graph families, including graph coloring, are similarly straightforward on split graphs.
The conjugate gradient method canalso be used to solve unconstrained optimization problems such as energy minimization.
LibreOffice Calc's solver component lets you solve optimization problems in which the optimum value of a particular spreadsheet cell has to be calculated based on constraints provided in other cells.
Another goal is to research newapplications of quantum technology to solve complex optimization problems and abstract physical models.
These systems essentially solve complex combinatorial optimization problems by exploring a huge number of possibilities to find the best possible value, that is, the optimal solution.
Branch and bound(BB, B&B, or BnB) is an algorithm designparadigm for discrete and combinatorial optimization problems, as well as mathematical optimization. .
It is often the most convenient(if not the most efficient[citation needed]) technique for parsing,[4]for the knapsack problem and other combinatorial optimization problems.
Take advantage of optionaladvanced modules on applications of maths to simulation and optimization problems, applied statistics(lead by active researchers), and physics.
Well, it is hugely beneficial, as every industry from automotive, finance, healthcare, retail to the publicsector faces countless complex combinatorial optimization problems.
Over the past few decades, evolutionary computing methods, especially genetic algorithms are used countless times for analytically difficult orintractable optimization problems to approximate a globally optimal solution.
Another school of thought is that the behaviour of a PSO swarm is not well understood in terms of how it affects actual optimization performance,especially for higher-dimensional search-spaces and optimization problems that may be discontinuous, noisy, and time-varying.
And ends with the optimization problem.
In this scenario, the corresponding constrained optimization problem can be written as follows.
And we obtain an optimization problem.
We now consider the following multiobjective optimization problem.
As a kind of optimization problem.
Travelling salesman problem is a very important combinatorial optimization problem.
Some sort of optimization problem.
The Travelling Salesperson Problem(TSP) is a computationally difficult combinatorial optimization problem.
The optimization problem permits approximation and is approximable within a O(| V|/ log| V|){\displaystyle O\ left(| V|/{\ sqrt{\log|V|}}\right)} approximation ratio.