add author name

machine_learning/adaline_learning.cpp

@@ -3,6 +3,8 @@
  * \brief [Adaptive Linear Neuron
  * (ADALINE)](https://en.wikipedia.org/wiki/ADALINE) implementation
  *
+ * \author [Krishna Vedala](https://github.com/kvedala)
+ *
  * <img
  * src="https://upload.wikimedia.org/wikipedia/commons/b/be/Adaline_flow_chart.gif"
  * width="200px">

machine_learning/kohonen_som_trace.cpp

@@ -8,6 +8,8 @@
  * follows the given data points. This this creates a chain of nodes that
  * resembles the given input shape.
  *
+ * \author [Krishna Vedala](https://github.com/kvedala)
+ *
  * \note This C++ version of the program is considerable slower than its [C
  * counterpart](https://github.com/kvedala/C/blob/master/machine_learning/kohonen_som_trace.c)
  * \note The compiled code is much slower when compiled with MS Visual C++ 2019

math/fibonacci_fast.cpp

@@ -11,6 +11,7 @@
  * found if we have already found n/2th or (n+1)/2th fibonacci It is a property
  * of fibonacci similar to matrix exponentiation.
  *
+ * \author [Krishna Vedala](https://github.com/kvedala)
  * @see fibonacci_large.cpp, fibonacci.cpp, string_fibonacci.cpp
  */
 

math/fibonacci_large.cpp

@@ -7,6 +7,7 @@
  * Took 0.608246 seconds to compute 50,000^th Fibonacci
  * number that contains 10450 digits!
  *
+ * \author [Krishna Vedala](https://github.com/kvedala)
  * @see fibonacci.cpp, fibonacci_fast.cpp, string_fibonacci.cpp
  */
 

math/large_factorial.cpp

@@ -2,6 +2,7 @@
  * @file
  * @brief Compute factorial of any arbitratily large number/
  *
+ * \author [Krishna Vedala](https://github.com/kvedala)
  * @see factorial.cpp
  */
 #include <cstring>

math/large_number.h

@@ -2,6 +2,7 @@
  * @file
  * @brief Library to perform arithmatic operations on arbitrarily large
  * numbers.
+ * \author [Krishna Vedala](https://github.com/kvedala)
  */
 
 #ifndef MATH_LARGE_NUMBER_H_

math/realtime_stats.cpp

@@ -5,6 +5,7 @@
  * This algorithm is really beneficial to compute statistics on data read in
  * realtime. For example, devices reading biometrics data. The algorithm is
  * simple enough to be easily implemented in an embedded system.
+ * \author [Krishna Vedala](https://github.com/kvedala)
  */
 #include <cassert>
 #include <cmath>

math/sqrt_double.cpp

@@ -1,7 +1,7 @@
 /**
  * @file
- * @brief Calculate the square root of any positive number in \f$O(\log N)\f$
- * time, with precision fixed using [bisection
+ * @brief Calculate the square root of any positive real number in \f$O(\log
+ * N)\f$ time, with precision fixed using [bisection
  * method](https://en.wikipedia.org/wiki/Bisection_method) of root-finding.
  *
  * @see Can be implemented using faster and better algorithms like

numerical_methods/durand_kerner_roots.cpp

@@ -3,6 +3,7 @@
  * \brief Compute all possible approximate roots of any given polynomial using
  * [Durand Kerner
  * algorithm](https://en.wikipedia.org/wiki/Durand%E2%80%93Kerner_method)
+ * \author [Krishna Vedala](https://github.com/kvedala)
  *
  * Test the algorithm online:
  * https://gist.github.com/kvedala/27f1b0b6502af935f6917673ec43bcd7

numerical_methods/newton_raphson_method.cpp

@@ -1,13 +1,15 @@
 /**
  * \file
  * \brief Solve the equation \f$f(x)=0\f$ using [Newton-Raphson
- * method](https://en.wikipedia.org/wiki/Newton%27s_method)
+ * method](https://en.wikipedia.org/wiki/Newton%27s_method) for both real and
+ * complex solutions
  *
  * The \f$(i+1)^\text{th}\f$ approximation is given by:
  * \f[
  * x_{i+1} = x_i - \frac{f(x_i)}{f'(x_i)}
  * \f]
  *
+ * \author [Krishna Vedala](https://github.com/kvedala)
  * \see bisection_method.cpp, false_position.cpp
  */
 #include <cmath>

numerical_methods/ordinary_least_squares_regressor.cpp

@@ -3,6 +3,7 @@
  * \brief Linear regression example using [Ordinary least
  * squares](https://en.wikipedia.org/wiki/Ordinary_least_squares)
  *
+ * \author [Krishna Vedala](https://github.com/kvedala)
  * Program that gets the number of data samples and number of features per
  * sample along with output per sample. It applies OLS regression to compute
  * the regression output for additional test data samples.

sorting/shell_sort2.cpp

@@ -1,6 +1,7 @@
 /**
  * \file
  * \brief [Shell sort](https://en.wikipedia.org/wiki/Shell_sort) algorithm
+ * \author [Krishna Vedala](https://github.com/kvedala)
  */
 #include <array>
 #include <cstdlib>