algorithm speed meaning in Chinese
算法的速度
Examples
- This new algorithm speed up the image formation more significantly than the original one
本文还分析了impsarwb - p这一特殊的成像算法的方位分辨率。 - The algorithm speeds up network study train , compared with the real - time recurrent learning algorithm consumedly , and increases the accuracy of prediction
与已有的实时循环学习算法相比，极大的提高了网络学习训练的速度，并且提高了预测的精度。 - The migration algorithm proposed by this paper allow that the execution of process migrating and process going is concurring to the most degree . the migration algorithm speeds up the migration , reduces the communication overhead and avoids residual dependencies
本文基于进程迁移的三个条件提出的进程迁移算法,最大程度地将进程状态迁移和进程的运行并行起来,从而提高了迁移速度,网络通信量也较小,而且也没有对源节点的残余依赖性。 - In the post - process , uses the microsoft medias technical to design cartoon display mode , it raises cartoon speed and saves disc space , it can produce majority prevailing medias form . in interpolation , introduced dsi method , which improve the interpolation method of isoline , raise algorithm speed and efficiency , satisfy the real - time quality request . on the basis of scanning line algorithm , use opengl smooth interpolate method to improve the implementati
在后处理过程中，引进了dsi插值方法，改进了以往等值线插值，提高了算法速度和效率，满足了实时性要求，在扫描线算法的基础上，引进了opm沁l插值方法，改进了云图实现方法，提高了图形显示精度，在动画格式上，我们可以根掘自己需要实现不同精度的不同压缩格式动画文件，提高了后处理显示应用场合，改变了以往动画格式单一的形式。 - Computing result shows that : coevolutionary mdo algorithms are effective on this problem ; distributed coevolutionary mdo algorithm is better than cooperate coevolutionary mdo algorithm ; asynchronous parallel version of distributed coevolutionary mdo algorithm speeds up the optimization procedure greatly while maintains good convergence performance ; multiobjective distributed coevolutionary mdo algorithm approximates the whole pareto optimal front well in only one single run , saves much computing cost than constraint method to obtain pareto optimal set , and greatly shortens search time by distributed asyn
计算结果表明：协同进化mdo算法求解该问题是有效的，其中分布式协同进化mdo算法优于合作协同进化mdo算法；异步并行的分布式协同进化mdo算法在保证收敛性能的同时大大加快了优化进程；多目标的分布式协同进化mdo算法仅一次运行就很好的逼近了问题的整个pareto最优前沿，比用约束法求解pareto最优集节省了大量计算开销，而且通过网络多台微机的分布式并行执行大大缩短了搜索时间。