 # Greedy algorithms doubt

Hello , can any one please tell me how to be good at greedy algorithms . How can we be sure about the correctness of our thinking for greedy algorithms . I’m practicing problems but i’m unable to come to a perfect claim for a particular greedy algorithm based question ?

Thanks in advance 2 Likes

We are on the same boat , i also want to know this very badly.

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There is actually a good framework of rules one can follow for most of the cases. Thats a pretty long answer.

But the best answer to give here is by developing an ability to come up with counter cases. I think you should solve a few B and C level problems on Codeforces (tags hidden for obvious reasons) and you will get the “framework” I am talking about.

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I never specifically focused on greedy algos…you just solve problems and it becomes easy soon.

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These are both good answers. Combining them: with any problem that requires you to find a strategy (“how do we place the minimum number of <things> so that <objective>”), one of the first questions you should ask yourself is “can I be greedy?”. You should then, as @vijju123 says, try to find counter examples (quite often, randomised testing again a simple, known-working brute-force approach will expose them pretty quickly). As with anything in maths/ logic, if you can’t find counter-examples, try to ask yourself “why can’t I find them”?

Having said all that, Hackerrank have a section specifically dealing with Greedy algorithms if you just want to practice your Greediness exclusively 5 Likes

Thanks bro for all this,

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Regarding this - proofs of correctness always involve some degree of creativity, so it’s hard to come up with general advice.

However, a broad approach that seems to work is as follows:

Say you are tasked with coming up with an optimal approach to a problem (placing telegraph poles to achieve a certain coverage with some kind of notion of a “cost” for a placement, for example) and wish to prove its optimality. Imagine that you have some optimal placement P that doesn’t adhere to your strategy. It must have some kind of difference from a placement that would arise from your strategy - can you find the “first” such difference? Perhaps there is some i such that the first i-1 telegraph poles match your strategy, and the i^\text{th} does not. Is there a way of modifying P so that it has the same (or better!) cost, but where now the first i telegraphs are placed according to your strategy?

As a specific example, here’s my solution to Hackerrank’s Mandragora Forest.

Mandragora Forest Solution and Write-Up
// Simon St James (ssjgz) - 2019-05-21
#define SUBMISSION
#ifdef SUBMISSION
#define NDEBUG
#endif
#include <iostream>
#include <vector>
#include <algorithm>

using namespace std;

int64_t findBestExperience(const vector<int>& originalH)
{
const int n = originalH.size();

int64_t bestExperience = 0;
auto sortedH = originalH;
sort(sortedH.begin(), sortedH.end());

vector<int64_t> sumOfRemainingHealths(n + 1);
int64_t sumOfLastHealths = 0;
for (const auto h : sortedH)
{
sumOfLastHealths += h;
}
for (int numRemaining = n; numRemaining >= 0; numRemaining--)
{
sumOfRemainingHealths[numRemaining] = sumOfLastHealths;
if (n - numRemaining >= 0)
sumOfLastHealths -= sortedH[n - numRemaining];
}

int64_t currentHealthIfOnlyEat = 1;
const int64_t currentExperienceIfOnlyEat = 0;
for (int i = 0; i < n; i++)
{
const int64_t currentExperience = currentHealthIfOnlyEat * sumOfRemainingHealths[n - i];
bestExperience = max(bestExperience, currentExperience);
currentHealthIfOnlyEat++;
}
return bestExperience;
}

int main(int argc, char* argv[])
{
// Very easy one, as evidenced by the ~70% success rate :)
//
// So: our task is to a) choose the order of Mandragoras to
// fight and b) choose whether to Eat or Battle each of these ordered Mandragoras
// such that we get the largest possible experience.   There are n! ways
// to order them, and for each order, 2**n ways to choose whether to
// Eat or Battle each Mandragora.  Could there be a better solution that this O(n! x 2**n) one??
//
// Obviously, yes ;) It seems intuitively obvious that we might want to Eat the lowest-health
// Mandragoras until we built up enough health to maximise the score from Battling the
// highest-health ones, and this is indeed the case, as we will see.
//
// Observation: given an ordered list of k Mandragoras (H(j_1), H(j_2), ... , H(j_k)), the maximum
// *gain* in experience from Eating/ Battling each in turn does not depend on the current
// experience, though it *does* depend on our current health; thus if we currently have h health
// it makes sense to define:
//
//   maxExpIncrease(h, (H(j_1), H(j_2), ... , H(j_k)))
//
// as the maximum increase in experience we can gain if our current health is h and we must
// Eat/ Battle, in order, the Mandragoras with healths H(j_1), H(j_2), ... , H(j_k).  To
// solve this challenge, we must compute:
//
//   initialExperience + maxExpIncrease(initialHealth, (H(i_1), H(i_2), ... , H(i_n)))
//
//   = maxExpIncrease(0, (H(i_1), H(i_2), ... , H(i_n)))
//
// across all permutations i_1, i_2, ... , i_n of 1, 2, ... , n.
//
// Theorem 1
//
// In a strategy that gives the largest experience gain, we may assume that the permutation
// i_1, i_2, ... , i_n of 1, 2, ... , n is the one that places the healths of the Mandragoras
// we face into increasing order i.e. H(i_1) <= H(i_2) <= ... <= H(i_n).
//
// Proof
//
// Pick any optimal strategy, and let i_1, i_2, ... , i_n of 1, 2, ... , n be the order of
// Mandragora's chosen for this strategy.  If this sorts H, then we're done; otherwise, pick
// the smallest j such that H(i_(j+1)) < H(i_j).
//
//   H(i_1) H(i_2) ... H(i_j) H(i_(j+1)) H(i_(j+2)) ... H(i_n)
//
// Let h be the current health and e the current experience at the moment after we dealt with
// the (j-1)th Mandragora.
//
// There are four possible scenarios for this strategy:
//
// i) We Battle H(i_j) and Battle H(i_(j+1)).
//
// The total experience for this strategy would then be:
//
//   e + h * H(i_j) + h * H(i_(j+1)) + maxExpIncrease(h, (H(i_(j+2)), H(i_(j+3)), ... , H(i_n))) (O1)
//
// Imagine if we changed this strategy so that H(i_j) and H(i_(j+1)) were swapped, but we still
// Battled both H(i_j) and H(i_(j + 1)).
//
//   H(i_1) H(i_2) ... H(i_(j+1)) H(i_j) H(i_(j+2)) ... H(i_n)
//                     ^Battle    ^Battle
//
// The total experience from this modified strategy is then:
//
//   e + h * H(i_(j+1)) + h * H(i_j)) + maxExpIncrease(h, (H(i_(j+2)), H(i_(j+3)), ... , H(i_n)))
//
// i.e. exactly the same as that of the original strategy, (O1).  Thus, in this case, we can make the order of
// Mandragora's "more ordered" with respect to health, but not lose any experience points from
// doing so.
//
// ii) We Battle H(i_j), and Eat (H_(j+1)).
//
// The total experience for this strategy would then be:
//
//   e + h * H(i_j) + 0 + maxExpIncrease(h + 1, (H(i_(j+2)), H(i_(j+3)), ... , H(i_n))) (O2)
//
// Imagine if we changed this strategy so that H(i_j) and H(i_(j+1)) were swapped, but we still
// Battle H(i_j) and Eat H(i_(j + 1)).
//
//   H(i_1) H(i_2) ... H(i_(j+1)) H(i_j) H(i_(j+2)) ... H(i_n)
//                     ^Eat       ^Battle
//
// The total experience for this modified strategy would then be:
//
//   e + 0 + (h + 1) * H(i_j) + maxExpIncrease(h + 1, (H(i_(j+2)), H(i_(j+3)), ... , H(i_n)))
//
// Subtracting (O2) from this gives:
//
//   H(i_j)
//
// and since H(i_j) >= 1, this is strictly better than (O2).  Thus, in this case, we can make the order of
// Mandragora's "more ordered" with respect to health, and actually get a better total experience from
// doing so.
//
// iii) We Eat H(i_j) and Battle H(i_(j+1))
//
// The total experience for this strategy would then be:
//
//   e + 0 + (h + 1) * H(i_(j+1)) + maxExpIncrease(h + 1, (H(i_(j+2)), H(i_(j+3)), ... , H(i_n))) (O3)
//
// Imagine if we changed this strategy so that H(i_j) and H(i_(j+1)) were swapped, but we now
// Battle H(i_j) and Eat H(i_(j + 1)).
//
//   H(i_1) H(i_2) ... H(i_(j+1)) H(i_j) H(i_(j+2)) ... H(i_n)
//                     ^Eat       ^Battle
//
// The total experience for this modified strategy would then be:
//
//   e + 0 + (h + 1) * H(i_j) + maxExpIncrease(h + 1, (H(i_(j+2)), H(i_(j+3)), ... , H(i_n)))
//
// Subtracting (O3) from this gives:
//
//   (h + 1) (H(i_j)) - H_(i_(j+1))
//
// and since H_(i_(j+1)) < H(i_j) by assumption, this is positive i.e. again in this case, we can make the order of
// Mandragora's "more ordered" with respect to health, and actually get a better total experience from
// doing so.
//
// iv) We Eat H(i_j) and Eat H(i_(j+1))
//
// The total experience for this strategy would then be:
//
//   e + 0 + 0 + maxExpIncrease(h + 2, (H(i_(j+2)), H(i_(j+3)), ... , H(i_n))) (O4)
//
// Imagine if we changed this strategy so that H(i_j) and H(i_(j+1)) were swapped, but we still
// Eat H(i_j) and Eat H(i_(j + 1)).
//
// The total experience for this modified strategy would then be:
//
//   e + 0 + 0 + maxExpIncrease(h + 2, (H(i_(j+2)), H(i_(j+3)), ... , H(i_n)))
//
// i.e. identical to (O4).
//
// Thus, in all four cases, we can increase the index of the first disordered pair of healths and end
// up with a total experience at least as good as the original; thus, by repeated application,
// any optimal strategy that does not sort the Mandragora's in order of health can be transformed into
// a strategy at least as good that does sort the Mandragora's in order of health.
//
// QED
//
// Theorem 2
//
// For any optimal strategy, we can assume that the Mandragora's are ordered in order of increasing health,
// and that we initially exclusively Eat Mandragora's until a certain point, after which we exclusively
// Battle the remaining Mandragora's.
//
// Proof
//
// We can assume that such a strategy has the Mandragora's ordered so that H(i_1) <= H(i_2) <= ... <= H(i_n)
// by Theorem 1.
//
// If a chosen optimal strategy is not of the form "initially exclusively Eat Mandragora's until a certain point,
// after which we exclusively Battle the remaining Mandragora's",  then there is some lowest j such that
// the strategy Battles H(i_j), but then Eats H(i_(j+1)).
//
// Again, let h and e be the health and experience after dealing with the (j-1)th Mandragora.  The total experience
// gained from our chosen strategy is then:
//
//    e + h * H(i_j) + 0 + maxExpIncrease(h + 1, (H(i_(j+2)), H(i_(j+3)), ... , H(i_n))) (OBE)
//
// Imagine if we instead Eat H(i_j) and then Battle H(i_(j+1)).  Then the total experience from this modified strategy
// is:
//
//    e + 0 + (h + 1) * H(i_(j+1)) + maxExpIncrease(h + 1, (H(i_(j+2)), H(i_(j+3)), ... , H(i_n)))
//
// Subtracting (OBE) from this gives:
//
//    (h + 1) * H(i_(j+1)) - h * H(i_j) >= (h + 1) * H(i_j) - h * H(i_j) (as H's are in increasing order)
//                                      =   H(i_j)
//                                      >=  1 (as all H's are >= 1)
//                                      > 0
//
// i.e. our modified strategy is actually better than the original one, and is closer to the "exclusively Eat then
// exclusively Battle".  Repeated application of this shows that we can turn any strategy into one at least
// as good that exclusively Eats then exclusively Battles.
//
// QED.
//
// So, almost there: we should sort our H's, then for each k = 0, 1, 2, ... n, compute:
//
//    experience from strategy that Eats first k, then Battles remaining.
//
// For any k, this is easily computed: after eating the first k, our health is 1 + k and our experience still 0.
// The remaining (n - k) each give current health * Health of remaining Mandragora so the total experience for this k
// is  (1 + k) * (sum of remaining (n - k) healths).  With a simple lookup table (sumOfRemainingHealths), we can easy compute this for all
// k and compute the best resulting experience all in O(n).  Easy-peasy :)

int T;
cin >> T;

for (int t = 0; t < T; t++)
{
int n;
cin >> n;

vector<int> H(n);
for (int i = 0; i < n; i++)
{
cin >> H[i];
}

cout << findBestExperience(H) << endl;
}
}


This is classed as “Dynamic Programming”, but the strategy for choosing the monster ordering at least is classic Greediness. Check out how the Proof of the optimality of the chosen strategy proceeds I can’t remember the specific problems, but I’ve used this general approach successfully several times now. Edit: Ah - I think Airports is one: https://www.hackerrank.com/challenges/airports/submissions/code/108282231

 “Mandragora Forest” is one of the easier problems on Hackerrank, with a ~70% success rate i.e. if you could teach a houseplant to type, it would probably get the right answer.

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Thanks bro 1 Like