spaghetti-source

#include <iostream>
#include <vector>
#include <functional>
#include <algorithm>

using namespace std;

#define fst first
#define snd second

spaghetti-source

%{{{
\documentclass[a4paper,11pt]{article} % single column
%\documentclass[a4paper,11pt,twocolumn]{article} % two columns

\usepackage[dvipdfmx]{graphicx}
\usepackage{latexsym}
\usepackage{amsthm,amsmath,amssymb}
\usepackage{enumerate}
\usepackage{multirow}
\usepackage{algorithm,algpseudocode}

spaghetti-source

//
// 最適解： AVG BY FETCH JOIN MAX PAD SIZE SQL SUM WORK （34文字）
// 計算にかかった時間：0.01[s]
// 解法：整数計画（IP）
//
// 解説：
//   重み付き集合被覆問題．問題のサイズが小さいのでIPで解ける．
//   ソルバーとして ILOG/CPLEX 12.5 を使用した．
//

spaghetti-source

// Maximum Cut 
//   modified version of Sahni and Gonzales's greedy heuristics
//   by S. Kahruman, E. Kolotoglu, S. Butenko, and I. V. Hicks
//
//   http://ise.tamu.edu/people/faculty/butenko/papers/maxcut.pdf

#include <iostream>
#include <vector>
#include <cstdio>
#include <cstdlib>

spaghetti-source

// 
// (Simplified) Sequence Memoizer 
//   for (unbounded depth) Kneser-Ney smoothing
//
// Reference: 
//
// - J. Gasthaus, F. Wood, and Y. W. Teh (2010):
//   Lossless compression based on the Sequence Memoizer.
//   In Proceedings of  Data Compression Conference,
//   pp. 337-345.

spaghetti-source

function D = FloydWarshall(D)
  n = size(D, 1);	
  for k = 1 : n
    i2k = repmat(D(:,k), 1, n);
    k2j = repmat(D(k,:), n, 1);
    D = min(D, i2k + k2j);
  end
end

spaghetti-source

#pragma once
#include <vector>

typedef unsigned short WORD;
typedef unsigned long DWORD;
typedef long           LONG;
typedef unsigned char  BYTE;
#pragma pack(push, 1)
struct BITMAPFILEHEADER {
  WORD  bfType;

spaghetti-source

// Runtime error if compile with "g++ -O3 main.cc"
#include <iostream>
#include <Eigen/Eigen>

using namespace std;
using namespace Eigen;

void f() {
  MatrixXd U(2,2);
  U = U*U; // runtime error

spaghetti-source

// 
// Zero-suppressed binary decision diagram with family algebra operations
//
// References: 
//   S. Minato (1993): 
//     Zero-suppressed BDDs for set manipulation in combinatorial problems.
//     Proceedings of the 30st annual Design Automation Conference, pp. 272-277.
//   S. Minato (1994):
//     Calculation of unate cube set algebra using zero-suppressed BDDs.
//     Proceedings of the 31st annual Design Automation Conference, pp. 420-424.

spaghetti-source

%
% solve   min c'x s.t. Ax >= b, x in {0,1}^n
% 
% Lagrangian L(x,u) := cx + ub - uAx
%
% original problem:
%   min[x] max[u] L(x,u) 
% Lagrangian dual
%   max[u] min[x] L(x,u)
%