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% hw5_steepdes.m
%
% Math 571 HW 5 Q1.
%
% Script to compute and plot objective function value, norm of gradient of f(x), number of function and gradient evaluations, and runtime of using steepest descent method with backtracking line search to minimize
% f(x) = e^T* (e + A_1*(D_1*x + d_1).^2) + A_2*(D_2*x + d_2).^2).^1/2)


load 'MinSurfProb.mat'
tic;

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% hw5_newtoncg.m
%
% Math 571 HW 5 Q1.
%
% Script to compute and plot objective function value, norm of gradient of f(x), number of function and gradient evaluations, and runtime of using Newton CG to minimize
% f(x) = e^T* (e + A_1*(D_1*x + d_1).^2) + A_2*(D_2*x + d_2).^2).^1/2)
% CG finds descent direction p_i that are conjugate to each other when
% iterating x_k+1 = x_k + alpha*p_k

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# First we get our data.
mydata <- read.table("usparty.txt")

names(mydata) # Lets us see all the variable names.
#attach(mydata) # This puts the variable names in memory. We will not be using this.

mysubsetdata<-subset(mydata, select=c(YEAR, MTOTCONG)) #This keeps only the two variables that we need.
summary(mysubsetdata) # Since no variables are listed, a summary for all variables in the data frame is printed.

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"YEAR" "RCONG" "DCONG" "RPRES" "DPRES" "G" "PRCONT" "PDCONT" "TOTPRES" "TOTCONG" "RREP" "DREP" "TR" "INFLAT" "PREVINFL" "PREVGNP" "GNP" "TOTPOP18" "ELIGIBLE" "MRCONG" "MDCONG" "MRPRES" "MDPRES" "MTOTCONG" "MTOTPRES" "MTR" "VRCONG" "VDCONG" "VRPRES" "VDPRES" "VTOTCONG" "VTOTPRES" "VTR" "LMRCONG" "LMDCONG" "LRREP" "LDREP" "REPRATIO" "DRATIO" "RRATIO" "REPSHIFT" "MRATIO1" "MRATIO2" "ON" "ONR1" "ONR2" "OND1" "OND2" "ON1" "ON2" "OFF" "OFF1" "OFF2" "PRESDIF" "RPRESDIF" "LRPDIF" "LPRESDIF" "LMTCONG" "DIFOYTC" "SHIFT" "FDR" "CPOINT" "CPCGNP" "CGNP" "LGNP" "LINFLAT"
"1" 1896 6845000 6339000 7102246 6492599 1 1 0 13907000 14652000 244 105 0 25 25 62.6 61.3 40981000 19332000 0.354076143182288 0.327901924270639 0.367382888475067 0.335847248086075 0.757914338919926 0.719377198427478 0 0.467171717171717 0.432637182637183 0.484728774228774 0.443120324870325 1 0.9491536991537 0 NA NA NA NA 0.398280802292264 0.300859598853868 0.699140401146132 NA 0.757914338919926 0 1 1 0 1 0 1 0 0 0 0 1.63126631434887e-09 1.63126631434887e-0

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// 与同学写的二叉搜索树（Binary Search Tree), 只是一部分用来做一个类似facebook的terminal程序。课程期末项目。

public class BSTFaceIndex {
	
	Node root; //represents the first Profile in the BST

	//constructor to initialize BST 	
	public BSTFaceIndex() {
		root = null; 
	}

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# ACM 比赛 https://open.kattis.com/problems/secretmessage

import sys
import math

strings = [s.strip() for s in sys.stdin.readlines()]

for i in range(1,int(strings[0]) + 1):
    # find input size for matrix and number of characters for each word in sentence
    s = strings[i]