Examples of using Optimization problems in English and their translations into Chinese
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Optimization problems in signal processing.
A lot of bugs, optimization problems, Alexander.”.
Deeper neural nets often yield harder optimization problems.
Management science, optimization problems, mathematical modeling.
In theory, you could solve a large number of optimization problems.
The optimization problems may have many local minima.
And a range of other NP-hard combinatorial optimization problems.
Solve combinatorial optimization problems such as the TSP.
Another intriguing direction involves solving complex optimization problems.
Optimization problems involve finding the best possible solution from all feasible solutions.
PSO learns from the scenario and uses it to solve the optimization problems.
Really, any minimax optimization problems of the form counts as adversarial learning to me.
PSO learned from the scenario and used it to solve the optimization problems.
Optimization problems are faced across industries including software design, logistics, finance, web search, genomics, and more.
Most machine learning problems can be posed as optimization problems.
The optimization problems expect you to select a feasible solution so that the value of the required function is minimized or maximized.
It enables normal Java™ programmers to solve optimization problems efficiently.
However, optimization problems are often so complex that it is impossible to know if design iterations are pushing things in the right direction.
Evolutionary computation for dynamic optimization problems.
Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions;
Unlike other quantum computers, D-Wave is suitable only for solving certain tasks,known as optimization problems.
D-Wave's computers can solve large data analytics and optimization problems more quickly than traditional digital computers.
These problems, which require the best combination of variables and solutions,are often called optimization problems.
This characteristic is called non-stationary or dynamic optimization problems and neural networks are not particularly good at handling them.
Our quantum hardware group is working on these improvements which willmake it easier for users to input hard optimization problems.
In fact,experts agree that today's supercomputers can solve some optimization problems on par with today's quantum annealing machines.
The scope of this dedicated workshop is toexplore the opportunities for the application of quantum technology and optimization problems in networked systems.
The Google researchers say that their quantumcomputer may also have uses in optimization problems, machine learning as well as materials science and chemistry.
The framework integrates temporal and structural constraints andavoids common optimization problems inherent in such high dimensional models.
This problem is known as chance-constrained knapsack problem andchance-constrained optimization problems have so far gained little attention in the evolutionary computation literature.