Maximum power estimation is a NP-hard problem.
最大功耗估计问题是一个NP难题。
Recent studies show that the problem is NP-hard.
最近的研究成果表明这一问题是NP难问题。
We show that both the reverse problems are strongly NP-hard.
我们将证明这两个逆网络选址问题都是强np困难的。
Test set problem is a NP-hard problem with wide applications.
测试集问题是一个有着广泛应用的NP难问题。
But it is a NP-Hard problem to get the minimal attribute reduction.
但求取任意问题的最小属性集是一个NP难问题。
The max-cut problem is a standard NP-hard problem in graph graphic theory.
最大割问题是图论中的一个典型的NP困难问题。
The minimum distance decoding of linear codes is NP-hard, but some solvable cases exist.
线性分组码的最小距离译码是NP -难问题，但有些情况可解。
It has been proved that computing most of these new vulnerability parameters are NP-hard.
但已被证明的是计算一般图的这些参数是NP-困难问题。
It is known that the problem is APX-hard for general graphs and NP-hard for planar graphs.
此问题在一般图下是APX困难问题，在平面图下是NP困难问题。
Solving NP-hard problems is always the bottleneck task in the field of computer science and technology.
求解np难度问题一直是计算机科学技术的一个瓶颈任务。
The vehicle-scheduling problem with time window is also a NP-hard problem being more complicated than VSP.
带有时间窗的车辆优化调度问题是比VSP复杂程度更高的NP难题。
The problem is known to he NP-hard when the waiting-time extension coefficients have two arbitrary values.
在两合机器的情况下，当延伸系数允许取两个不同值时，该问题已被证明是难问题。
Layout optimization is an NP-hard problem. It also belongs to complex nonlinear constrained optimization problem.
布局优化是NP难问题，也是复杂的非线性约束优化问题。
It is difficult to solve in large scale because the LRP is a typical NP-hard problem in combinatorial optimization.
由于定位-车辆路线问题是组合优化问题中一个典型的NP难题，大规模时难以精确求解。
The modern scheduling problems are more complex than classical scheduling problems, most of which are NP-hard problems.
这些现代排序问题比经典排序问题更为复杂，绝大多数都是NP-难问题。
The problem of maximizing total weighted satisfaction level for single machine with fuzzy due-date is a NP-hard problem.
单机模糊交货期总加权满意程度最大化问题是一个NP -难问题。
In this paper we propose there efficient arithmetic-weal search, much space search and overall situation search for sowing NP-hard problem.
提出三种有效的快速算法——局部搜索、多空间搜索和全局搜索来解决NP难度问题。
In complexity theory, set packing problems is an important NP-hard problem, which is used widely in the fields of scheduling and code optimization.
在复杂性理论中，此问题是一类重要的NP难问题，被广泛应用于调度、代码优化和生物信息学等领域。
There are a lot of NP-hard optimization problems in the engineering application, which are difficult to solve by traditional mathematical techniques.
在实际工程应用中有很多优化问题是NP难问题，难以应用传统数学方法来解决。
It is easily shown that this problem is NP-hard, and a dynamic programming algorithm and a branch-and-bound algorithm are developed to solve it optimally.
文中简单说明此问题为NP -难问题，并提出了一种动态规划算法和一种分枝定界算法来求得此问题的最优解。
As a combinatorial optimization problem, three dimension packing problem with multi-constraints is NP-HARD problem. It is very difficult to obtain an optimal solution.
作为一类组合优化问题，多约束条件下的三维装载问题属于NP - Hard问题，其求解也是相当困难的。
Finding an optimal scheduling for such an environment is a NP-hard problem, and so heuristic approaches must be used in general to get an optimal approximation solution.
由于在这样的环境中找到一个最优的调度是一个NP难问题，通常运用各种启发式算法来找到近似最优解。
But the RWA in mesh networks is a NP-hard problem which can not be solved optimally with exhaustive search in the endurable time. Hence, we designed a heuristic algorithm to solve it.
而格状网络中的RWA是个NP -难问题，它不可能在有限的时间内通过穷举搜索来获得其优化解，为此我们设计了一个启发性算法以解决这个问题。
It has been proved the computation of minimal reduction and full reduction both is NP-hard problem, in artificial intelligence the common way is to employ heuristic knowledge to reduce.
研究表明，最小约简的计算和全部约简的求算都是NP问题，在人工智能中，解决这类问题的一般方法是利用启发式信息进行约简。
The fill in problem for graphs is a well known NP hard problem.
图的最小填充问题是熟知的NP-困难问题。
Solving NP hard problems is always the bottleneck task for computer science and techniques.
求解np难问题一直是计算机科学技术中的一个瓶颈任务。
The feature subset selection is an important problem in machine learning, but the optimal feature subset selection is proves to be a NP hard one.
特征子集选择问题是机器学习的重要问题。而最优特征子集的选择是NP困难问题，因此需要启发式搜索指导求解。
The problem is known as NP hard no matter whether group sub lotting is admissible or not.
无论是对于成组加工情形还是分组情形，该问题都可以被证明是NP困难的。
The problem is known as NP hard no matter whether group sub lotting is admissible or not.
无论是对于成组加工情形还是分组情形，该问题都可以被证明是NP困难的。
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