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Sum range using loop, calculate sum of odd and even numbers

Hi I am sick of searching I could not find the exact code for my question.
I need to code the sum of the odd numbers from 1 to 100
and sum of the even numbers from 2 to 100.
This is what i have so far.
Thank you so much
// 1) using for statement to Sum Up a Range of values using Interactive
Console.WriteLine(" Sum Up a Range of values entered by User ");
Console.WriteLine();
// 2) Declare the Variables to be used in the Project
string strFromNumber, strToNumber;
int fromNumber, toNumber;
int sum = 0;
int i, even = 0, odd = 0;
int[] array = new int[10];
// 3) Prompt the User to Enter the From Number to Sum From
Console.Write("Enter the From Number to Sum From: ");
strFromNumber = Console.ReadLine();
fromNumber = Convert.ToInt32(strFromNumber);
// 4) Prompt the User to Enter the To Number to Sum To
Console.Write("Enter the To Number to Sum To: ");
strToNumber = Console.ReadLine();
toNumber = Convert.ToInt32(strToNumber);
// 5) Use for statement to Sum up the Range of Numbers
for (i = fromNumber; i <= toNumber; ++i)
{
sum += i;
}
if //(array[i] % 2 == 0) //here if condition to check number
{ // is divided by 2 or not
even = even + array[i]; //here sum of even numbers will be stored in even
}
else
{
odd = odd + array[i]; //here sum of odd numbers will be stored in odd.
}
Console.WriteLine("The Sum of Values from {0} till {1} = {2}",
fromNumber, toNumber, sum);
Console.ReadLine();

There is no need to write the complex code which you have written.
Problem is to calculate the sum of arithmetic progression. The formula to find the sum of an arithmetic progression is Sn = n/2[2a + (n − 1) × d] where, a = first term of arithmetic progression, n = number of terms in the arithmetic progression and d = common difference.
So in case of odd numbers its a = 1, n = 50 and d = 2
and in case of even numbers its a = 2, n = 50 and d = 2
and if you try to normalize these above formulas, it will be more easy based on your problem.
the sum of the first n odd numbers is Sn= n^2
the sum of the first n even numbers is n(n+1).
and obviously, it's very simple to loop from ( 1 to 99 with an increment of 2 ) and ( 2 to 100 with an increment of 2 )

In the simplest case, you can try looping in fromNumber .. toNumber range while adding
number either to even or to odd sum:
// long : since sum of int's can be large (beyond int.MaxValue) let's use long
long evenSum = 0;
long oddSum = 0;
for (int number = fromNumber; number <= toNumber; ++number) {
if (number % 2 == 0)
evenSum += number;
else
oddSum += number;
}
Console.WriteLine($"The Sum of Values from {fromNumber} till {toNumber}");
Console.WriteLine($"is {evenSum + oddSum}: {evenSum} (even) + {oddSum} (odd).");
Note, that you can compute both sums in one go with a help of arithmetics progression:
private static (long even, long odd) ComputeSums(long from, long to) {
if (to < from)
return (0, 0); // Or throw ArgumentOutOfRangeException
long total = (to + from) * (to - from + 1) / 2;
from = from / 2 * 2 + 1;
to = (to + 1) / 2 * 2 - 1;
long odd = (to + from) / 2 * ((to - from) / 2 + 1);
return (total - odd, odd);
}
Then
(long evenSum, long oddSum) = ComputeSums(fromNumber, toNumber);
Console.WriteLine($"The Sum of Values from {fromNumber} till {toNumber}");
Console.WriteLine($"is {evenSum + oddSum}: {evenSum} (even) + {oddSum} (odd).");

From the code snippet you shared, it seems like the user gives the range on which the sum is calculated. Adding to #vivek-nuna's answer,
Let's say the sum of the first N odd numbers is given by, f(n) = n^2 and
the sum of the first N even numbers is given by, g(n) = n(n + 1).
So the sum of odd numbers from (l, r) = f(r) - f(l - 1).
Similarly, the sum of even numbers from (l, r) = g(r) - g(l - 1).
Hope this helps.

C# WinsForm, Frequency Distribution Table [Updated]

Update 01
Thanks to Caius, found the main problem, the logic on the "if" was wrong, now fixed and giving the correct results. The loop still create more positions than needed on the secondary List, an extra position for each number on the main List.
I've updated the code bellow for refence for the following question:
-001 I can figure out why it create positions that needed, the for loop should run only after the foreach finishes its loops correct?
-002 To kind of solving this issue, I've used a List.Remove() to remove all the 0's, so far no crashes, but, the fact that I'm creating the extra indexes, and than removing them, does means a big performance down if I have large list of numbers? Or is an acceptable solution?
Description
It supposed to read all numbers in a central List1 (numberList), and count how many numbers are inside a certain (0|-15 / 15|-20) range, for that I use another List, that each range is a position on the List2 (numberSubList), where each number on List2, tells how many numbers exists inside that range.
-The range changes as the numbers grows or decrease
Code:
void Frequency()
{
int minNumb = numberList.Min();
int maxNumb = numberList.Max();
int size = numberList.Count();
numberSubList.Clear();
dGrdVFrequency.Rows.Clear();
dGrdVFrequency.Refresh();
double k = (1 + 3.3 * Math.Log10(size));
double h = (maxNumb - minNumb) / k;
lblH.Text = $"H: {Math.Round(h, 2)} / Rounded = {Math.Round(h / 5) * 5}";
lblK.Text = $"K: {Math.Round(k, 4)}";
if (h <= 5) { h = 5; }
else { h = Math.Round(h / 5) * 5; }
int counter = 1;
for (int i = 0; i < size; i++)
{
numberSubList.Add(0); // 001 HERE, creating more positions than needed, each per number.
foreach (int number in numberList)
{
if (number >= (h * i) + minNumb && number < (h * (i + 1)) + minNumb)
{
numberSubList[i] = counter++;
}
}
numberSubList.Remove(0); // 002-This to remove all the extra 0's that are created.
counter = 1;
}
txtBoxSubNum.Clear();
foreach (int number in numberSubList)
{
txtBoxSubNum.AppendText($"{number.ToString()} , ");
}
lblSubTotalIndex.Text = $"Total in List: {numberSubList.Count()}";
lblSubSumIndex.Text = $"Sum of List: {numberSubList.Sum()}";
int inc = 0;
int sum = 0;
foreach (int number in numberSubList)
{
sum = sum + number;
int n = dGrdVFrequency.Rows.Add();
dGrdVFrequency.Rows[n].Cells[0].Value = $"{(h * inc) + minNumb} |- {(h * (1 + inc)) + minNumb}";
dGrdVFrequency.Rows[n].Cells[1].Value = $"{number}";
dGrdVFrequency.Rows[n].Cells[2].Value = $"{sum}";
dGrdVFrequency.Rows[n].Cells[3].Value = $"{(number * 100) / size} %";
dGrdVFrequency.Rows[n].Cells[4].Value = $"{(sum * 100) / size} %";
inc++;
}
}
Screen shot showing the updated version.

I think, if your aim is to only store eg 17 in the "15 to 25" slot, this is wonky:
if (number <= (h * i) + minNumb) // Check if number is smaller than the range limit
Because it's found inside a loop that will move on to the next range, "25 to 35" and it only asks if the number 17 is less than the upper limit (and 17 is less than 35) so 17 is accorded to the 25-35 range too
FWIW the range a number should be in can be derived from the number, with (number - min) / number_of_ranges - at the moment you create your eg 10 ranges and then you visit each number 10 times looking to put it in a range, so you do 9 times more operations than you really need to

how many numbers between 1 to 10 billion contains 14

i tried this code but it takes so long and I can not get the result
public long getCounter([FromBody]object req)
{
JObject param = Utility.GetRequestParameter(req);
long input = long.Parse(param["input"].ToString());
long counter = 0;
for (long i = 14; i <= input; i++)
{
string s = i.ToString();
if (s.Contains("14"))
{
counter += 1;
}
}
return counter;
}
please help

We can examine all non-negative numbers < 10^10. Every such number can be represented with the sequence of 10 digits (with leading zeroes allowed).
How many numbers include 14
Dynamic programming solution. Let's find the number of sequences of a specific length that ends with the specific digit and contains (or not) subsequence 14:
F(len, digit, 0) is the number of sequences of length len that ends with digit and do not contain 14, F(len, digit, 1) is the number of such sequences that contain 14. Initially F(0, 0, 0) = 1. The result is the sum of all F(10, digit, 1).
C++ code to play with: https://ideone.com/2aS17v. The answer seems to be 872348501.
How many times the numbers include 14
First, let's place 14 at the end of the sequence:
????????14
Every '?' can be replaced with any digit from 0 to 9. Thus, there are 10^8 numbers in the interval that contains 14 at the end. Then consider ???????14?, ??????14??, ..., 14???????? numbers. There are 9 possible locations of 14 sequence. The answer is 10^8 * 9 = 90000000.
[Added by Matthew Watson]
Here's the C# version of the C++ implementation; it runs in less than 100ms:
using System;
namespace Demo
{
public static class Program
{
public static void Main(string[] args)
{
const int M = 10;
int[,,] f = new int [M + 1, 10, 2];
f[0, 0, 0] = 1;
for (int len = 1; len <= M; ++len)
{
for (int d = 0; d <= 9; ++d)
{
for (int j = 0; j <= 9; ++j)
{
f[len,d,0] += f[len - 1,j,0];
f[len,d,1] += f[len - 1,j,1];
}
}
f[len,4,0] -= f[len - 1,1,0];
f[len,4,1] += f[len - 1,1,0];
}
int sum = 0;
for (int i = 0; i <= 9; ++i)
sum += f[M,i,1];
Console.WriteLine(sum); // 872,348,501
}
}
}

If you want a brute force solution it could be something like this (please, notice, that we should avoid time consuming string operations like ToString, Contains):
int count = 0;
// Let's use all CPU's cores: Parallel.For
Parallel.For(0L, 10000000000L, (v) => {
for (long x = v; x > 10; x /= 10) {
// Get rid of ToString and Contains here
if (x % 100 == 14) {
Interlocked.Increment(ref count); // We want an atomic (thread safe) operation
break;
}
}
});
Console.Write(count);
It returns 872348501 within 6 min (Core i7 with 4 cores at 3.2GHz)

UPDATE
My parallel code calculated the result as 872,348,501 in 9 minutes on my 8- processor-core Intel Core I7 PC.
(There is a much better solution above that takes less than 100ms, but I shall leave this answer here since it provides corroborating evidence for the fast answer.)
You can use multiple threads (one per processor core) to reduce the calculation time.
At first I thought that I could use AsParallel() to speed this up - however, it turns out that you can't use AsParallel() on sequences with more than 2^31 items.
(For completeness I'm including my faulty implementation using AsParallel at the end of this answer).
Instead, I've written some custom code to break the problem down into a number of chunks equal to the number of processors:
using System;
using System.Linq;
using System.Threading.Tasks;
namespace Demo
{
class Program
{
static void Main()
{
int numProcessors = Environment.ProcessorCount;
Task<long>[] results = new Task<long>[numProcessors];
long count = 10000000000;
long elementsPerProcessor = count / numProcessors;
for (int i = 0; i < numProcessors; ++i)
{
long end;
long start = i * elementsPerProcessor;
if (i != (numProcessors - 1))
end = start + elementsPerProcessor;
else // Last thread - go right up to the last element.
end = count;
results[i] = Task.Run(() => processElements(start, end));
}
long sum = results.Select(r => r.Result).Sum();
Console.WriteLine(sum);
}
static long processElements(long inclusiveStart, long exclusiveEnd)
{
long total = 0;
for (long i = inclusiveStart; i < exclusiveEnd; ++i)
if (i.ToString().Contains("14"))
++total;
return total;
}
}
}
The following code does NOT work because AsParallel() doesn't work on sequences with more than 2^31 items.
static void Main(string[] args)
{
var numbersContaining14 =
from number in numbers(0, 100000000000).AsParallel()
where number.ToString().Contains("14")
select number;
Console.WriteLine(numbersContaining14.LongCount());
}
static IEnumerable<long> numbers(long first, long count)
{
for (long i = first, last = first + count; i < last; ++i)
yield return i;
}

You compute the count of numbers of a given length ending in 1, 4 or something else that don't contain 14. Then you can extend the length by 1.
Then the count of numbers that do contain 14 is the count of all numbers minus those that don't contain a 14.
private static long Count(int len) {
long e1=0, e4=0, eo=1;
long N=1;
for (int n=0; n<len; n++) {
long ne1 = e4+e1+eo, ne4 = e4+eo, neo = 8*(e1+e4+eo);
e1 = ne1; e4 = ne4; eo = neo;
N *= 10;
}
return N - e1 - e4 - eo;
}
You can reduce this code a little, noting that eo = 8*e1 except for the first iteration, and then avoiding the local variables.
private static long Count(int len) {
long e1=1, e4=1, N=10;
for (int n=1; n<len; n++) {
e4 += 8*e1;
e1 += e4;
N *= 10;
}
return N - 9*e1 - e4;
}
For both of these, Count(10) returns 872348501.

One easy way to calculate the answer is,
You can fix 14 at a place and count the combination of the remaining numbers right to it,
and do this for all the possible positions where 14 can be place such that the number is still less than 10000000000,Lets take a example,
***14*****,
Here the '*' before 14 can be filled by 900 ways and the * after 14 can be filled by 10^5 ways so total occurrence will be 10^5*(900),
Similarly you can fix 14 at other positions to calculate the result and this solution will be very fast O(10) or simply O(1), while the previous solution was O(N), where N is 10000000000

You can use the fact that in each 1000 (that is from 1 to 1000 and from 1001 to 2000 etc)
the 14 is found: 19 times so when you receive your input divide it by 1000 for example you received 1200 so 1200/1000
the result is 1 and remainder 200, so we have 1 * 19 "14"s and then you can loop over the 200.
you can extend for 10000 (that is count how many "14"s there are in 10000 and fix it to a global variable) and start dividing by 10000 then and apply the equation above, then you divide the remainder by 1000 and apply the equation and add the two results.
You can extend it as the fact that for all hundreds (that is from 1 to 100 and from 201 to 300) the "14" is found only 1 except for the second hundred (101 to 200).

C# Triangle numbers optimization

The task is to find a triangle number which has at least 500 divisors.
For example 28 has 6 divisors: 1,2,4,7,14,28
My code works for up to 200 divisors, but for 500 it runs forever...
Is there any way to optimize the code. For instance I thought of dynamic optimization and memoization, but couldn't find a way to do it?
int sum = 0;
int counter = 0;
int count = 1;
bool isTrue = true;
while (isTrue)
{
counter = 0;
sum += count;
for (int j = 1; j <= sum; j++)
{
if (sum % j == 0)
{
counter++;
if (counter == 500)
{
isTrue = false;
Console.WriteLine("Triangle number: {0}", sum);
break;
}
}
}
count++;
}
Console.WriteLine("Number of divisors: {0}", counter);

Ignore the fact that the number is a triangle number. If you can solve this problem quickly:
given any number n, determine the number of divisors it has
then obviously you can solve Euler #12 quickly. Just list the triangle numbers, which are easy to calculate, determine the number of divisors of each, and stop when you get a result 500 or larger.
So how do you determine the number of divisors quickly? As you've discovered, when the numbers get big, it's a lot of work.
Here's a hint. Suppose you already have the prime factorization. Let's pick a number, say, 196. Factorize that into prime numbers:
196 = 2 x 2 x 7 x 7
I can tell you just by glancing at the factorization that 196 has nine divisors. How?
Because any divisor of 196 is of the form:
(1, 2 or 2x2) x (1, 7 or 7x7)
So obviously there are nine possible combinations:
1 x 1
1 x 7
1 x 7 x 7
2 x 1
2 x 7
2 x 7 x 7
2 x 2 x 1
2 x 2 x 7
2 x 2 x 7 x 7
Pick another number. 200, lets say. Thats 2 x 2 x 2 x 5 x 5. So there are twelve possibilities:
1 x 1
1 x 5
1 x 5 x 5
2 x 1
2 x 5
...
2 x 2 x 2 x 5 x 5
See the pattern? You take the prime factorization, group them by prime, and count how many are in each group. Then you add one to each of those numbers and multiply them together. Again, in 200 there are three twos and two fives in the prime factorization. Add one to each: four and three. Multiply them together: twelve. That's how many divisors there are.
So you can find the number of divisors very quickly if you know the prime factorization. We have reduced the divisor problem to a much easier problem: Can you figure out how to produce a prime factorization quickly?

here are some optimizations I'll just throw out there for you.
the easiest thing is to change
for (int j = 1; j <= sum; j++)
{
if (sum % j == 0)
{
counter++;
if (counter == 500)
{
isTrue = false;
Console.WriteLine("Triangle number: {0}", sum);
break;
}
}
}
if you've found 1 divisor, you've found 2 divisors, so change it to
for (int j = 1; j <= sum; j++)
{
if (sum % j == 0)
{
if(sum/j < j)
break;
else if(sum/j == j)
counter++;
else
counter +=2;
if (counter == 500)
{
isTrue = false;
Console.WriteLine("Triangle number: {0}", sum);
break;
}
}
}
this will reduce the runtime a lot, but it will still take a long time.
another optimization you can do is to not start checking form sum but calculate the smallest number that has 500 divisors.
and then you can find the largest triangle number after that, and start from there.
If you can figure something else special about the nature of this problem, than it is possible for you to reduce the runtime by a whole lot.

The number of divisors of a number is the product of the powers of the prime factors plus one. For example: 28 = 2^2*7^1, so the # of divisors is (2+1)*(1+1) = 6.
This means that, if you want many divisors compared to the size of the number, you don't want any one prime to occur too often. Put another way: it is likely that the smallest triangular number with at least 500 divisors is the product of small powers of small primes.
So, instead of checking every number to see if it divides the triangular number, go through a list of the smallest primes, and see how often each one occurs in the prime factorization. Then use the formula above to compute the number of divisors.

Take these steps:
1.) Calculate the first log(2, 499) prime numbers (not 500, as 1 is counted as a divisor if I am nit mistaken despite the fact that it is not prime, as it has only one divisor). There are many solutions out there, but you catch my drift.
2.) A triangle number is of the form of n * (n + 1) / 2, because
1 + 2 + ... + 100 = (1 + 100) + (2 + 99) + ... + (50 + 51) = 101 * 50 = 101 * 100 / 2 = 5050 (as Cauchy solved it when he was an eight-year boy and the teacher punished him with this task).
1 + ... + n = (1 + n) + (2 + n - 1) + ... = n * (n + 1) / 2.
3.) S = prod(first log(2, 499) prime numbers)
4.) Solve the equation of n * (n + 1) / 2 = S and calculate its ceiling. You will have an integer, let's call it m.
5.)
while (not(found))
found = isCorrect(m)
if (not(found)) then
m = m + 1
end if
end while
return m
and there you go. Let me know if I was able to help you.

As #EricLippert nad #LajosArpad mentioned, the key idea is to iterate over triangle numbers only. You can calculate them using the following formula:
T(n) = n * (n + 1) / 2
Here is JSFiddle which you may find helpful.
function generateTriangleNumber(n) {
return (n * (n + 1)) / 2;
}
function findTriangleNumberWithOver500Divisors() {
var nextTriangleNum;
var sqrt;
for (i = 2;; i++) {
var factors = [];
factors[0] = 1;
nextTriangleNum = generateTriangleNumber(i);
sqrt = Math.pow(nextTriangleNum, 0.5);
sqrt = Math.floor(sqrt);
var j;
for (j = 2; j <= sqrt; j++) {
if (nextTriangleNum % j == 0) {
var quotient = nextTriangleNum / j;
factors[factors.length] = j;
factors[factors.length] = quotient;
}
}
factors[factors.length] = nextTriangleNum;
if (factors.length > 500) {
break;
}
}
console.log(nextTriangleNum);
}

Incidentally, the first Google result for divisors of triangular number search query gives this :)
Project Euler 12: Triangle Number with 500 Divisors
See if it helps.
EDIT: Text from that article:
The first triangle number with over 500 digits is: 76576500 Solution
took 1 ms

Math.Log vs multiplication complexity in terms of geometric average which is better?

I want to find geometric average of data and performance does matters.
Which one should I pick between
Keep multiplication over single variable and take Nth-root at the end of calculation
X = MUL(x[i])^(1/N)
Thus, O(N) x Multiplication Complexity + O(1) x Nth-root
Use logarithm
X = e ^ { 1/N * SUM(log(x[i])) }
Thus, O(N) x Logarithm Complexity + O(1) x Nth-division + O(1) Exponential
Specialized algorithm for geometric average. Please tell me if there is.

I thought I would try to benchmark this and get a comparison, here is my attempt.
Comparing was difficult since the list of numbers needed to be large enough to make timing it reasonable, so N is large. In my test N = 50,000,000 elements.
However, multiplying lots of numbers together which are greater than 1 overflows the double storing the product. But multiplying together numbers less than 1 gives a total product which is very small, and dividing by the number of elements gives zero.
Just a couple more things: Make sure none of your elements are zero, and the Log approach doesn't work for negative elements.
(The multiply would work without overflow if C# had a BigDecimal class with an Nth root function.)
Anyway, in my code each element is between 1 and 1.00001
On the other hand, the log approach had no problems with overflows, or underflows.
Here's the code:
class Program
{
static void Main(string[] args)
{
Console.WriteLine("Starting...");
Console.WriteLine("");
Stopwatch watch1 = new Stopwatch();
Stopwatch watch2 = new Stopwatch();
List<double> list = getList();
double prod = 1;
double mean1 = -1;
for (int c = 0; c < 2; c++)
{
watch1.Reset();
watch1.Start();
prod = 1;
foreach (double d in list)
{
prod *= d;
}
mean1 = Math.Pow(prod, 1.0 / (double)list.Count);
watch1.Stop();
}
double mean2 = -1;
for (int c = 0; c < 2; c++)
{
watch2.Reset();
watch2.Start();
double sum = 0;
foreach (double d in list)
{
double logged = Math.Log(d, 2);
sum += logged;
}
sum *= 1.0 / (double)list.Count;
mean2 = Math.Pow(2.0, sum);
watch2.Stop();
}
Console.WriteLine("First way gave: " + mean1);
Console.WriteLine("Other way gave: " + mean2);
Console.WriteLine("First way took: " + watch1.ElapsedMilliseconds + " milliseconds.");
Console.WriteLine("Other way took: " + watch2.ElapsedMilliseconds + " milliseconds.");
Console.WriteLine("");
Console.WriteLine("Press enter to exit");
Console.ReadLine();
}
private static List<double> getList()
{
List<double> result = new List<double>();
Random rand = new Random();
for (int i = 0; i < 50000000; i++)
{
result.Add( rand.NextDouble() / 100000.0 + 1);
}
return result;
}
}
My computer output describes that both geometric means are the same, but that:
Multiply way took: 466 milliseconds
Logarithm way took: 3245 milliseconds
So, the multiply appears to be faster.
But multiply is very problematic with overflow and underflow, so I would recommend the Log approach, unless you can guarantee the product won't overflow and that the product won't get too close to zero.