cshjin

# OEIS sequence id A360497: https://oeis.org/A360497
# 
# Author: Hongwei Jin <jinh@anl.gov>
# Date: 2023-07-26

from sympy import isprime
import matplotlib.pyplot as plt
import networkx as nx

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# Dataset

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# defaultSettings.yaml for latexindent.pl, version 3.7.1, 2019-09-07
#                      a script that aims to
#                      beautify .tex, .sty, .cls files
#
# (or latexindent.exe if you're on Windows)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

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try:
    # import tf 2.X
    import tensorflow.compat.v2 as tf
except Exception:
    # import tf 1.X
    import tensorflow as tf
    tf.logging.set_verbosity(tf.logging.ERROR)
print('Using TF {}'.format(tf.__version__))

# set logging level for tf

cshjin

Collection of Coding Practice

This is a collection of online programming practice. Several website provide online judge, some provide problem list and some others provide only result judge.

List of online resources

Website	OJ	RJ	Practice	Challenge	Interviews	Languages
CareerCup	❌	❌	❌	⭕	⭕	C, C++, Java, Python, etc.
CodeChef	⭕	❌	⭕	⭕	⭕	C, C++, Java, Python, etc.

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# -*- coding: utf-8 -*-
#!/bin/env python
import numpy as np
import unittest


def softmax(x):
    """ Given a vector, apply the softmax activation function 

    Parameters

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""" Solve a minimax problem 
    The problme is defined as
    $$ \min_{x} \max_{y} f(x, y) = x^2(y+1) + y^2(x+1)$$
    
    The first order gradient is 
    $$ \frac{\partial f}{\partial x} = 2x(y+1) + y^2 $$
    $$ \frac{\partial f}{\partial y} = x^2 + 2y(x+1) $$
    
    From the first order optimality condition, the alternatively solver 
        should solve the problem and converge to a stationary point.

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yalmip('clear')

T = [0 1 0 1; 1 0 1 0; 0 1 0 1; 1 0 1 0];

x = sdpvar(4, 1);
y = sdpvar(4, 1);
assign(x, randn(4, 1));
assign(y, randn(4, 1));
const = [x'*x <= 1; y'*y <=1];
% const = [];

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import numpy as np
import pickle
import matplotlib.pyplot as plt
import scipy.io as sio
import tensorflow as tf

tf.enable_eager_execution()
tmm = tf.matmul

def _test():

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import numpy as np
import sys
# reference: https://en.wikipedia.org/wiki/Power_iteration

def power_method(M):
    b = np.random.rand(M.shape[1])
    diff = np.linalg.norm(b)
    while diff > 10 ** (-6):
        b_new = np.dot(M, b)
        b_norm = np.linalg.norm(b_new)