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FFT
- // 入口参数： // l: l = 0, 傅立叶变换 l = 1, 逆傅立叶变换 // il: il = 0,不计算傅立叶变换或逆变换模和幅角；il = 1,计算模和幅角 // n: 输入的点数，为偶数，一般为32，64，128，...,1024等 // k: 满足n=2^k(k>0),实质上k是n个采样数据可以分解为偶次幂和奇次幂的次数 // pr[]: l=0时，存放N点采样数据的实部 // l=1时, 存放傅立叶变换的N个实部 // pi
FFTReal
- FFT源码，快速傅里叶变换和逆变换，Portable ISO C++开发的完整类-FFTReal Version 1.03, 2001/06/15 Class of Fourier transformation of real data (FFT and IFFT) Portable ISO C++ (c) Laurent de Soras <ldesoras@club-internet.fr>
FR
- 用c++写的共轭梯度法程序，有求在某点的梯度的函数，和偏导数-Using c++ to write the conjugate gradient method procedures, the gradient at some point demand function, and the partial derivatives
ed_new
- energy detion fr cognitive radio
dcx
- 用FR共轭梯度法求解无约束最优化问题（c++）-With the FR conjugate gradient method for solving unconstrained optimization problems
relay_test
- please dowload it fr-it is a very best example
FR
- 一种节无约束优化问题的算法，该算法有很大的局限性。-One kind of unconstrained optimization problems section algorithm has great limitations.
L1General
- L1General是一个求解各种L1-正则化问题的Matalb程序。详细说明可参考：http://www.di.ens.fr/~mschmidt/Software/L1General.html -L1General is a set of Matlab routines implementing several of the available strategies for solving L1-regularization problems.
FR
- 工程优化中用FR共轭梯度法求最值，采用的是教科书上的经典算法步骤，简单易懂，可以进行参考。-The most value engineering optimization using FR conjugate gradient method, using the classic steps of the algorithm in the textbook, easy to understand, you can reference.
NA=FEM_z=freecode_feelPP-0.90.0.tar
- FEM=Galerkin 有限元的代码._ gforge.imag.fr/-FEM = Galerkin finite element code. _ Gforge.imag.fr /
LSL_SC
- based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the mini
spgl1-1.8
- based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to
linear optimization
- 用BFGS 拟牛顿法 最速下降法 牛顿法 共轭梯度法 解决线性优化问题(Solving linear optimization problems with conjugate gradient method and Steepest descent method.)