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[Mathimatics-Numerical algorithms] Sy1 DL : 0: 用C语言实现的乘法器-C language Multiplier
Date : 2008-10-13 Size : 1.32kb User : 蒙蒙
[Mathimatics-Numerical algorithms] multiplier DL : 0: 求解约束优化问题增广拉格朗日乘子法源程序
Date : 2008-10-13 Size : 1.35kb User : dong
[Mathimatics-Numerical algorithms] Sy1 DL : 0: 用C语言实现的乘法器-C language Multiplier
Date : 2025-12-16 Size : 1kb User : 蒙蒙
[Mathimatics-Numerical algorithms] gaojingdujiechengsuanfa DL : 0: 高精度乘法基本思想和加法一样。其基本流程如下： ①读入被乘数s1，乘数s2 ②把s1、s2分成4位一段，转成数值存在数组a,b中；记下a,b的长度k1,k2； ③i赋为b中的最低位； ④从b中取出第i位与a相乘，累加到另一数组c中；（注意：累加时错开的位数应是多少位？） ⑤i:=i-1；检测i值：小于k2则转⑥,否则转④ ⑥打印结果-High-precision multiplication and addition the same basic idea. The basic process is as follows: ① read multiplicand s1, multiplier s2 ② to s1, s2 is divided into four section into the existence of an array of values a, b in write down a, b the length of k1, k2 ③ i given for the b of the lowest ④ Remove from b in the first i-bit and a multiply, accumulate to another array in c (Note: The median cumulative time should be staggered how many?) ⑤ i: = i-1 detection i value: less than k2 was transferred ⑥, otherwise switch to print the results ④ ⑥
Date : 2025-12-16 Size : 1kb User : Eurik
[Mathimatics-Numerical algorithms] multiplier DL : 0: Date : 2025-12-16 Size : 1kb User : dong
[Mathimatics-Numerical algorithms] Constrained_Optimization_and_Lagrange_Multiplier_M DL : 0: 一本介绍约束优化方面的经典书籍。对于从事约束优化算法的研究很有帮助。-A book introducing constrained programming. It is beneficial for the algorithm research of constrained programming
Date : 2025-12-16 Size : 10.99mb User : fd
[Mathimatics-Numerical algorithms] MAC DL : 0: Multiplier/Accumulator written in Verilog
Date : 2025-12-16 Size : 375kb User : binh
[Mathimatics-Numerical algorithms] multiplier_with_booth_coding DL : 0: 该进modified boot算法实现乘法器，很好，值得看一下，-The multiplier algorithm into the modified boot, well worth a look,
Date : 2025-12-16 Size : 1018kb User : kaiyongx
[Mathimatics-Numerical algorithms] Loeliger_factor_graph DL : 0: 消息传递算法用于信号处理的绝对经典之作，好好学习，天天向上-The message-passing approach to model-based signal processing is developed with a focus on Gaussian message passing in linear state-space models, which includes recursive least squares, linear minimum-mean-squared-error estimation, and Kalman filtering algorithms. Tabulated message computation rules for the building blocks of linear models allow us to compose a variety of such algorithms without additional derivations or computations. Beyond the Gaussian case, it is emphasized that the message-passing approach encourages us to mix and match different algorithmic techniques, which is exemplified by two different approachesV steepest descent and expectation maximizationVto message passing through a multiplier node.
Date : 2025-12-16 Size : 933kb User : wfs