Currency Arbitrage

11th JLTi Code Jam – Jan 2018

Here we revisit Manipulating Money Exchange problem where we tried to find currency arbitrage using Bellman-Ford at a time complexity of O(|V||E|).

In general, this kind of graph can be dense. Suppose, there are 4 currencies: USD, SGD, GBP and INR. Usually, a rate is given from each currency to all other currencies, resulting in |V|2 edges. Hence, Bellman-Ford ends up with O(|V||E|) = O(|V||V|2) = O(|V|3), that is quite expensive. Specially, when you consider the fact that apart from the few hundred fiat currencies, there are 1000+ cryptocurrencies out there.

Also we should not forget that currency exchange rate is not a factor of solely the currency itself, rather it is tied with an exchange. For example, suppose, Moneycorp exchange has a USD to SGD rate 1.4 while for HiFX it is 1.396 for the same. So we see, USD appearing twice in the graph – once as part of Moneycorp and again as part of HiFX.

However, computing shortest paths, a prerequisite for finding arbitrage, is something quite expensive. In this problem, we need to incrementally compute shortest paths when a new vertex, nth one arrives, assuming we have pre-computed results for (n-1) vertices that we can re-use.

To be more specific, suppose, at this moment, we have 12344 vertices and we already know whether there is an arbitrage, after computing the necessary shortest paths. And then, a new currency, JioCoin arrives with some new rates (from JioCoin to some existing currencies, say, JioCoin to INR and from some existing currencies to JioCoin, say, SGD to JioCoin). Now we have 12345 vertices. Computing shortest paths for a dense graph with 12345 vertices would take a very long time (try running 3 nested for loops, each looping 12345 times), doing billions of computations.

At this moment, would it be not wise to use the existing results for 12344 vertices? And then incrementally adjust the new shortest paths and compute some new ones? That is precisely, this problem is all about. We need to incrementally, adjust/add shortest paths once a new vertex arrives. And this is to be done at a time complexity of O(|V|2), something that is comfortably manageable. After this, we have to now say, whether an arbitrage exists.

Input:

1 USD = 1.380 SGD

1 SGD = 3.080 MYR

1 MYR = 15.120 INR

1 INR = 0.012 GBP

1 GBP = 1.30 USD

I CAD = 0.57 GBP

Explanation: Whenever a rate arrives, starting from the first, for each new vertex, we need to incrementally adjust/add shortest paths, find whether an arbitrage exists or not and output the same. We have 6 inputs here. Each time an input comes, we need to output and hence, we have 6 lines of output. The first 4 did not result in any arbitrage, we output “No luck here”. From 5th we have an arbitrage and we output the same.

Once an arbitrage is found, it is going to last. Note that, there might exist more than one arbitrage. Printing any one will do.

An important thing: rate between a certain currency pair will not appear twice in the input. Meaning once, GBP to USD rate arrives at line 5, a new rate between the two won’t arrive again.

Output:

No luck here

No luck here

No luck here

No luck here

USD -> SGD -> MYR -> INR -> GBP -> USD

USD -> SGD -> MYR -> INR -> GBP -> USD

Input:

1 USD = 1.38295 SGD

1 SGD = 3.08614 MYR

1 MYR = 15.0996 INR

1 INR = 0.0119755 GBP

1 GBP = 1.295 USD

Output:

No luck here

No luck here

No luck here

No luck here

No luck here

Task: For each line of input, for each new vertex, incrementally adjust/add shortest paths at a cost (time) of O(|V|2), detect the presence of an arbitrage and output as specified.

Index

Sprint Completion Time

10th JLTi Code Jam – Dec 2017

At the start of a sprint we are given a list of deliverables. The first thing in our mind is whether the team can deliver it in time. Thus estimating time to complete a sprint is something very important.

The first thing we do is, split the deliverables into a number of tasks, and estimate the time required to complete each of them. A task takes 3 days to complete means it takes 3 days for one person to complete; it cannot be split further to get 3 persons doing it on a single day.

While some tasks can be completed independently, others might be dependent tasks – meaning we cannot start them unless the prerequisite tasks are completed first. For example, work on a report cannot start until we are done with the database design/creation. Testing or deployment cannot be done unless we develop the solution. Suppose, completing task 1, and task 2 takes 4, and 6 days respectively and task 2 is dependent on task 1 – in other words – task 1 is a prerequisite for task 2. In this case, completing task 2 would take 10 days.

Finally, we don’t have an infinite number of people available. And for simplicity, assume each person is capable of doing any of the tasks.

Input:

3

4 3 2 1 4 6

1 2 4

2 3 4

4 3

5 6

6 3

Output: 12

Explanation:

The first line says the team has 3 persons. Second line lists the number of days required to complete each of the tasks. Here we have 6 numbers. It says we have 6 tasks – task 1 takes 4 days to complete, task 2 takes 3 days to complete and so on. The last, task 6, takes 6 days.

The subsequent lines list the dependent tasks. 1 2 4 means task 1 depends on tasks 2 and 4. 6 3 means task 3 is a prerequisite for task 6. No line starts with 3 means task 3 does not depend on any other task.

Task 3 can be completed in 2 days by one person. These first 2 days the other two persons have to sit idle as all other tasks are dependent tasks. After 2 days, task 2, 4 or 6 – all of which were dependent on task 3 can start. Each of the 3 persons can start any of them. Once task 6 is done task 5 can start. Similarly, when task 2 is done task 1 can start. We will see completing all of them takes 12 days.

Input:

2

4 3 6 2

1 2

2 3

3 1

Output: Infeasible

Explanation: We have 2 persons to complete 4 tasks – completing them take 4, 3, 6 and 2 days respectively.  However, we see that task 1 is dependent on task 2, task 2 is dependent on task 3 and task 3 is dependent back on task 1. While we can finish task 4 easily, we cannot start any of the first 3 tasks. They are dependent on each other and thus creating a dependency cycle.

Task: Manually calculating the minimum time required to complete the tasks is time consuming and prone to error, especially when we need to estimate this very often. Why not write a small program that can do it for us?

Index

RC Election Result

9th JLTi Code Jam – Nov 2017

Whole JLT is buzzing with Recreational Committee (RC) aka Fun Ministry election 2018. It is more palpable in JLTi where a fierce competition is taking place between two candidates representing Millennial Party and Traditional Party. In this two-party system, Millennial Party is claiming that they know the magic as to how people can be entertained while the Traditional Party cannot stop laughing at them saying they are just inexperienced kids incapable of running the massive Fun Ministry.

This time, voting mechanism has changed. Instead of one person one vote that was how it worked till last year, one person’s vote weight would now equal to the number of years he/she is working at JLT. For example, I am working here for 4 years and hence my vote would count as 4. If somebody is working for just 1 year, his/her vote weight would be 1. For obvious reason, Millennial Party is unhappy about this new legislation that was recently passed by the incumbent Traditional Party. They call it unfair. But law is law.

Voting stopped on 10th Nov 2017 and counting votes would commence on 13th Nov 2017 followed by the announcement of result on the same day.

Being a member of the existing RC, my concern is little different. I am worried about a tie, and if that happens, what would be the next course of action.

Hence, I am checking the possibility of a tie. Manually doing so is quite problematic, if not impossible, for several hundred employees that we have in Singapore. Being the only software engineer in the existing RC, I am tasked to write a program that would take vote weight of each of the voters as input and output whether a tie is possible.

Input:

20 10 4 6

Output: Possible

ExplanationVote weight 20 and 10 – sounds familiar? Anyway, we see that if the first voter votes for one candidate and the rests for another – a tie is inevitable.

Input:

8 7 2 5 16

Output: Not Possible

Explanation: As you see the total vote weight for the above 5 voters is 38. For a tie to happen each candidate should get a vote count of 19. However, we can see, no way a vote count of 19 is possible here.

Input:

5, 6, 7, 3

Output: Not Possible

Explanation: We see, the total vote weight for the above 4 voters is 21. An odd number cannot be divided by two.

Task: Given a list of vote weight, one for each voter, we need to find whether a tie is possible. We are assuming that all voters in the input would vote for sure.

Index

Solution – Choosing Oranges

38th Friday Fun Session – 3rd Nov 2017

Given a set of goodness scores of oranges and a window length, we need to find the highest scoring oranges within the window as we move it from left to the end.

This is the solution for JLTI Code Jam – Oct 2017.

Using priority queue

Suppose we have n scores and the window length is m. We can simply move the window from left to to right and take (consecutive) m scores within the window and each time compute the max of them, and output it, if it is already not outputted. Finding max from m scores would take O(m) and as we do it n times (n-m+1 times, to be precise), the complexity would be O(mn). However, it was expected that the complexity would be better than this.

Alternatively, we can use a max-heap, where we push each score as we encounter it. At the same time, we retrieve the top of the max-heap and if all is fine – output it. By if all is fine, we mean to say, we need to make sure that the orange has not been already outputted and that it belongs to the current window. At the same time, if the top item is out-dated, we can pop it, meaning take it out of the heap. Note that, max-heap is a data structure that retains the max element at the top.

Let us walk through an example

Let us take the first example as mentioned here. For the scores 1 3 5 7 3 5 9 1 2 5 with window size 5, let us walk through the process.

At first, we push the first 4 items (4 is one less than the window size 5). The max-heap would look like: 1 3 5 7 where 7 is the top element.

Then for each of the remaining items, we do the following:

  1. If the (new) item is greater than or equal to the top item in the max-heap, pop it (out) and push the new item into it. Output the new item (if the same orange is not already outputted). We do it because the new item is the max in the present window. And the existing top one is of no further use. We can now move to the next item.
  2. Keep on popping the top as long it is not one belonging to the current window. We do it, as we are interested to find the max within the window, not the out-dated ones those are no longer inside the window.
  3. Output the top (if the same orange is not already outputted). We do it as it the max within the current window.
  4. If the top item is the oldest (left-most/first/earliest/starting one) in the current window, pop it. We do it because this item is going to go out of the window as the next item gets in.

Score 3:

3 is not >= 7 (top in heap)

Existing top (7) is within the current window. Output it.

Push 3; max-heap looks like: 1 3 3 5 7

Score 5:

5 is not >= 7 (top in heap)

Existing top (7) is within the current window (3 5 7 3 5). But this orange is already outputted (we can use index of the item to track it, meaning instead of just pushing the score, retain the index along with it). No output this time.

Push 5; max-heap looks like: 1 3 3 5 5 7

Score 9:

9 >= 7 (top in heap)

Pop 7, push 9, output 9.

New max-heap: 1 3 3 5 5 9

Score 1:

1 is not >= 9 (top in heap)

Existing top (9) is within the current window. But this orange is already outputted. No output this time.

Push 1; max-heap looks like: 1 1 3 3 5 5 9

Score 2:

2 is not >= 9 (top in heap)

Existing top (9) is within the current window. But this orange is already outputted. No output this time.

Push 2; max-heap looks like: 1 1 2 3 3 5 5 9

Score 5:

Existing top (9) is within the current window. But this orange is already outputted. No output this time.

Push 5; max-heap looks like: 1 1 2 3 3 5 5 5 9

No item left. We are done!

Finally, output is 7, 9.

Complexity

If we closely observe, we see that the size of the max-heap would be always around m. Because, if the new item is greater or equal we are popping the top – hence the max-heap size is not increasing. If new item is smaller, we are pushing it and the size of the max-heap is increasing – true; but then soon the top would be out-dated and then we would pop that. So the max-heap size remains around m. Pushing (in) or popping (out) an item would cost log m, and since we would do it n times – the complexity would be O(n log m). Please note that getting the top of the max-heap costs O(1).

GitHub: Choosing Oranges

Index

Choosing Oranges

8th JLTi Code Jam – Oct 2017

Orange is one of my favourite fruits that I buy for our Friday Fun Session participants. How would you choose the good ones from hundreds of them; especially, on the way to office, when you stop by the supermarket, in the morning rush hour?

To speed up the selection while at the same time choosing the good – firm, smooth and heavier compare to its size – I have devised a selection process. I would go from left to right, scoring each of the oranges, in a scale from 0 to 9, 9 being the best; and once a row is done, I go to the next row and so on. As I go and score, I would also choose the best one among each consecutive, say 5 oranges.

How that would look like?

Input:

5

1 3 5 7 3 5 9 1 2 5

Output: 7, 9

Explanation:

The first line says: choose the best among consecutive 5. The second line shows the score for each of the 10 oranges. The first 5 are: 1, 3, 5, 7, and 3; best among them is 7. We choose 7. The next 5 are:  3, 5, 7, 3, and 5; best among them 7 – already chosen. Move on to the next 5: 5, 7, 3, 5, and 9; best among them 9, pick that. Move to the next 5: 7, 3, 5, 9, and 1; best among them is 9, already chosen. Next 5 are: 3, 5, 9, 1, and 2; once again the best among them 9 is already chosen. Final 5 are: 5, 9, 1, 2, and 5; same as before. We cannot move further as we don’t have 5 oranges after this point.

We end up with two oranges: 7 and 9. I am not doing a bad job of selecting the best oranges for you, am I?

Input:

4

1, 3, 5

Output: None

The first line says: choose the best among 4. However, the second line shows only 3 oranges. Obviously we cannot choose any.

Input:

3

1 2 4 9

Output: 4, 9

Choose 4 from 1, 2 and 4. And then choose 9 from the next consecutive 3: 2, 4 and 9. And we are done!

Task: If we have a total of n oranges and we got to choose the best from each consecutive m, I am looking for a solution having better than O(mn) time complexity.

Index

Graced by Your Presence


Friday Fun Session Participants

Those of us who participate(d) our weekly learning and discussion session:

  1. Bala Krishnan
  2. Tang Biao
  3. Vignesh Shankar
  4. Chia Wei Woo
  5. Mahadevan Hariharan
  6. Ramakrishnan Kalyanaraman
  7. William Lim
  8. Srila Das Bhattacharya
  9. Sravani Vanukuru
  10. Kristipi Valledor
  11. Jeffrey Quiatchon
  12. Jothi Kiruthika
  13. Sayed Neda Fatima
  14. Sreenivasulu Gotla
  15. Vishal Gupta
  16. French Jean Palma Jumawan
  17. Gopi Krishna Pasupuleti
  18. Htet Aung Nay
  19. Aquib Javed Momin
  20. Pravinkarthy Ravi
  21. Rishabh Mangal
  22. Sunil Koli
  23. Vikas Pai
  24. Sandip Dangat
  25. Hui Ling Chong
  26. Srinivasa Puchakayala Reddy
  27. Manikandan Chandran
  28. Sharon Wong
  29. Uma Maheswary Ganesan
  30. Ishwarya Sridharan
  31. Aristotle Tiru
  32. Balamurugan Chennarayaperumal
  33. Aarti Piskala Dhanabalan
  34. Karthik Kumar
  35. Sunil Khamkar
  36. Handy Toh Torres
  37. Daniel Vo
  38. Srinivasan Badri Prasad
  39. Parthasarathi Murugaiyan
  40. Hieu Nguyen Van
  41. Manikandan Panneerselvam
  42. Jayamaran Ayilu
  43. Muukta Kedar
  44. Gaurav Singh
  45. Vikas Kitawat
  46. Tanveer Shaikh
  47. Vishal Jain
  48. Dipti Saurabh Shindhe
  49. Samir Tank
  50. Bhushan Patil
  51. Munendra Tomar
  52. Prabakaran Boopathi
  53. Vikraman Sridharan
  54. Srikanth Rokkam
  55. Santhosh Kumar Janakiraman
  56. Christabel Merline
  57. Ankit Jain
  58. Neethila Arasi
  59. Hari Gopal Raman
  60. Gopal Chandra Das