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i have an appointment for university which is due today and i start getting nervous. We recently discussed dynamic programming for algorithm optimization and now we shall implement an algorithm ourself which uses dynamic programming.
Task
So we have a simple game for which we shall write an algorithm to find the best possible strategy to get the best possible score (assuming both players play optimized).
We have a row of numbers like 4 7 2 3 (note that according to the task description it is not asured that it always is an equal count of numbers). Now each player turnwise takes a number from the back or the front. When the last number is picked the numbers are summed up for each player and the resulting scores for each player are substracted from each other. The result is then the score for player 1. So an optimal order for the above numbers would be
P1: 3 -> p2: 4 -> p1: 7 -> p2: 2
So p1 would have 3, 7 and p2 would have 4, 2 which results in a final score of (3 + 7) - (4 + 2) = 4 for player 1.
In the first task we should simply implement "an easy recursive way of solving this" where i just used a minimax algorithm which seemed to be fine for the automated test. In the second task however i am stuck since we shall now work with dynamic programming techniques. The only hint i found was that in the task itself a matrix is mentioned.
What i know so far
We had an example of a word converting problem where such a matrix was used it was called Edit distance of two words which means how many changes (Insertions, Deletions, Substitutions) of letters does it take to change one word into another. There the two words where ordered as a table or matrix and for each combination of the word the distance would be calculated.
Example:
W H A T
| D | I
v v
W A N T
editing distance would be 2. And you had a table where each editing distance for each substring was displayed like this:
"" W H A T
1 2 3 4
W 1 0 1 2 3
A 2 1 1 2 3
N 3 2 2 2 3
T 4 3 3 3 2
So for example from WHA to WAN would take 2 edits: insert N and delete H, from WH to WAN would also take 2 edits: substitude H->A and insert N and so on. These values where calculated with an "OPT" function which i think stands for optimization.
I also leanred about bottom-up and top-down recursive schemes but im not quite sure how to attach that to my problem.
What i thought about
As a reminder i use the numbers 4 7 2 3.
i learned from the above that i should try to create a table where each possible result is displayed (like minimax just that it will be saved before). I then created a simple table where i tried to include the possible draws which can be made like this (which i think is my OPT function):
4 7 2 3
------------------
a. 4 | 0 -3 2 1
|
b. 7 | 3 0 5 4
|
c. 2 | -2 -5 0 -1
|
d. 3 | -1 -4 1 0
the left column marks player 1 draws, the upper row marks player 2 draws and each number then stands for numberP1 - numberP2. From this table i can at least read the above mentioned optimal strategy of 3 -> 4 -> 7 -> 2 (-1 + 5) so im sure that the table should contain all possible results, but im not quite sure now how to draw the results from it. I had the idea to start iterating over the rows and pick the one with the highest number in it and mark that as the pick from p1 (but that would be greedy anyways). p2 would then search this row for the lowest number and pick that specific entry which would then be the turn.
Example:
p1 picks row a. 7 | 3 0 5 4 since 5 is the highest value in the table. P2 now picks the 3 from that row because it is the lowest (the 0 is an invalid draw since it is the same number and you cant pick that twice) so the first turn would be 7 -> 4 but then i noticed that this draw is not possible since the 7 is not accessible from the start. So for each turn you have only 4 possibilities: the outer numbers of the table and the ones which are directly after/before them since these would be accessable after drawing. So for the first turn i only have rows a. or d. and from that p1 could pick:
4 which leaves p2 with 7 or 3. Or p1 takes 3 which leaves p2 with 4 or 2
But i dont really know how to draw a conclusion out of that and im really stuck.
So i would really like to know if im on the right way with that or if im overthinking this pretty much. Is this the right way to solve this?
The first thing you should try to write down, when starting a dynamic programming algorithm, is a recurrence relation.
Let's first simplify a very little the problem. We will consider that the number of cards is even, and that we want to design an optimal strategy for the first player to play. Once we have managed to solve this version of the problem, the others (odd number of cards, optimize strategy for second player) follows trivially.
So, first, a recurrence relation. Let X(i, j) be the best possible score that player 1 can expect (when player 2 plays optimally as well), when the cards remaining are from the i^th to the j^th ones. Then, the best score that player 1 can expect when playing the game will be represented by X(1, n).
We have:
X(i, j) = max(Arr[i] + X(i+1, j), X(i, j-1) + Arr[j]) if j-i % 2 == 1, meaning that the best score that player one can expect is the best between taking the card on the left, and taking the card on the right.
In the other case, the other player is playing, so he'll try to minimize:
X(i, j) = min(Arr[i] + X(i+1, j), X(i, j-1) + Arr[j]) if j-i % 2 == 0.
The terminal case is trivial: X(i, i) = Arr[i], meaning that when there is only one card, we just pick it, and that's all.
Now the algorithm without dynamic programming, here we only write the recurrence relation as a recursive algorithm:
function get_value(Arr, i, j) {
if i == j {
return Arr[i]
} else if j - i % 2 == 0 {
return max(
Arr[i] + get_value(i+1, j),
get_value(i, j-1) + Arr[j]
)
} else {
return min(
Arr[i] + get_value(i+1, j),
get_value(i, j-1) + Arr[j]
)
}
}
The problem with this function is that for some given i, j, there will be many redundant calculations of X(i, j). The essence of dynamic programming is to store intermediate results in order to prevent redundant calculations.
Algo with dynamic programming (X is initialized with + inf everywhere.
function get_value(Arr, X, i, j) {
if X[i][j] != +inf {
return X[i][j]
} else if i == j {
result = Arr[i]
} else if j - i % 2 == 0 {
result = max(
Arr[i] + get_value(i+1, j),
get_value(i, j-1) + Arr[j]
)
} else {
result = min(
Arr[i] + get_value(i+1, j),
get_value(i, j-1) + Arr[j]
)
}
X[i][j] = result
return result
}
As you can see the only difference with the algorithm above is that we now use a 2D array X to store intermediate results. The consequence on time complexity is huge, since the first algorithm runs in O(2^n), while the second runs in O(n²).
Dynamic programming problems can generally be solved in 2 ways, top down and bottom up.
Bottom up requires building a data structure from the simplest to the most complex case. This is harder to write, but offers the option of throwing away parts of the data that you know you won't need again. Top down requires writing a recursive function, and then memoizing. So bottom up can be more efficient, top down is usually easier to write.
I will show both. The naive approach can be:
def best_game(numbers):
if 0 == len(numbers):
return 0
else:
score_l = numbers[0] - best_game(numbers[1:])
score_r = numbers[-1] - best_game(numbers[0:-1])
return max(score_l, score_r)
But we're passing a lot of redundant data. So let's reorganize it slightly.
def best_game(numbers):
def _best_game(i, j):
if j <= i:
return 0
else:
score_l = numbers[i] - _best_game(i+1, j)
score_r = numbers[j-1] - _best_game(i, j-1)
return max(score_l, score_r)
return _best_game(0, len(numbers))
And now we can add a caching layer to memoize it:
def best_game(numbers):
seen = {}
def _best_game(i, j):
if j <= i:
return 0
elif (i, j) not in seen:
score_l = numbers[i] - _best_game(i+1, j)
score_r = numbers[j-1] - _best_game(i, j-1)
seen[(i, j)] = max(score_l, score_r)
return seen[(i, j)]
return _best_game(0, len(numbers))
This approach will be memory and time O(n^2).
Now bottom up.
def best_game(numbers):
# We start with scores for each 0 length game
# before, after, and between every pair of numbers.
# There are len(numbers)+1 of these, and all scores
# are 0.
scores = [0] * (len(numbers) + 1)
for i in range(len(numbers)):
# We will compute scores for all games of length i+1.
new_scores = []
for j in range(len(numbers) - i):
score_l = numbers[j] - scores[j+1]
score_r = numbers[j+i] - scores[j]
new_scores.append(max(score_l, score_r))
# And now we replace scores by new_scores.
scores = new_scores
return scores[0]
This is again O(n^2) time but only O(n) space. Because after I compute the games of length 1 I can throw away the games of length 0. Of length 2, I can throw away the games of length 1. And so on.
I received this interview question and got stuck on it:
There are an infinite number of train stops starting from station number 0.
There are an infinite number of trains. The nth train stops at all of the k * 2^(n - 1) stops where k is between 0 and infinity.
When n = 1, the first train stops at stops 0, 1, 2, 3, 4, 5, 6, etc.
When n = 2, the second train stops at stops 0, 2, 4, 6, 8, etc.
When n = 3, the third train stops at stops 0, 4, 8, 12, etc.
Given a start station number and end station number, return the minimum number of stops between them. You can use any of the trains to get from one stop to another stop.
For example, the minimum number of stops between start = 1 and end = 4 is 3 because we can get from 1 to 2 to 4.
I'm thinking about a dynamic programming solution that would store in dp[start][end] the minimum number of steps between start and end. We'd build up the array using start...mid1, mid1...mid2, mid2...mid3, ..., midn...end. But I wasn't able to get it to work. How do you solve this?
Clarifications:
Trains can only move forward from a lower number stop to a higher number stop.
A train can start at any station where it makes a stop at.
Trains can be boarded in any order. The n = 1 train can be boarded before or after boarding the n = 3 train.
Trains can be boarded multiple times. For example, it is permitted to board the n = 1 train, next board the n = 2 train, and finally board the n = 1 train again.
I don't think you need dynamic programming at all for this problem. It can basically be expressed by binary calculations.
If you convert the number of a station to binary it tells you right away how to get there from station 0, e.g.,
station 6 = 110
tells you that you need to take the n=3 train and the n=2 train each for one station. So the popcount of the binary representation tells you how many steps you need.
The next step is to figure out how to get from one station to another.
I´ll show this again by example. Say you want to get from station 7 to station 23.
station 7 = 00111
station 23 = 10111
The first thing you want to do is to get to an intermediate stop. This stop is specified by
(highest bits that are equal in start and end station) + (first different bit) + (filled up with zeros)
In our example the intermediate stop is 16 (10000). The steps you need to make can be calculated by the difference of that number and the start station (7 = 00111). In our example this yields
10000 - 00111 = 1001
Now you know, that you need 2 stops (n=1 train and n=4) to get from 7 to 16.
The remaining task is to get from 16 to 23, again this can be solved by the corresponding difference
10111 - 10000 = 00111
So, you need another 3 stops to go from 16 to 23 (n= 3, n= 2, n= 1). This gives you 5 stops in total, just using two binary differences and the popcount. The resulting path can be extracted from the bit representations 7 -> 8 -> 16 -> 20 -> 22 -> 23
Edit:
For further clarification of the intermediate stop let's assume we want to go from
station 5 = 101 to
station 7 = 111
the intermediate stop in this case will be 110, because
highest bits that are equal in start and end station = 1
first different bit = 1
filled up with zeros = 0
we need one step to go there (110 - 101 = 001) and one more to go from there to the end station (111 - 110 = 001).
About the intermediate stop
The concept of the intermediate stop is a bit clunky but I could not find a more elegant way in order to get the bit operations to work. The intermediate stop is the stop in between start and end where the highest level bit switches (that's why it is constructed the way it is). In this respect it is the stop at which the fastest train (between start and end) operates (actually all trains that you are able to catch stop there).
By subtracting the intermediate stop (bit representation) from the end station (bit representation) you reduce the problem to the simple case starting from station 0 (cf. first example of my answer).
By subtracting the start station from the intermediate stop you also reduce the problem to the simple case, but assume that you go from the intermediate stop to the start station which is equivalent to the other way round.
First, ask if you can go backward. It sounds like you can't, but as presented here (which may not reflect the question as you received it), the problem never gives an explicit direction for any of these trains. (I see you've now edited your question to say you can't go backward.)
Assuming you can't go backward, the strategy is simple: always take the highest-numbered available train that doesn't overshoot your destination.
Suppose you're at stop s, and the highest-numbered train that stops at your current location and doesn't overshoot is train k. Traveling once on train k will take you to stop s + 2^(k-1). There is no faster way to get to that stop, and no way to skip that stop - no lower-numbered trains skip any of train k's stops, and no higher-numbered trains stop between train k's stops, so you can't get on a higher-numbered train before you get there. Thus, train k is your best immediate move.
With this strategy in mind, most of the remaining optimization is a matter of efficient bit twiddling tricks to compute the number of stops without explicitly figuring out every stop on the route.
I will attempt to prove my algorithm is optimal.
The algorithm is "take the fastest train that doesn't overshoot your destination".
How many stops this is is a bit tricky.
Encode both stops as binary numbers. I claim that an identical prefix can be neglected; the problem of going from a to b is the same as the problem of going from a+2^n to b+2^n if 2^n > b, as the stops between 2^n and 2^(n+1) are just the stops between 0 and 2^n shifted over.
From this, we can reduce a trip from a to b to guarantee that the high bit of b is set, and the same "high" bit of a is not set.
To solve going from 5 (101) to 7 (111), we merely have to solve going from 1 (01) to 3 (11), then shift our stop numbers up 4 (100).
To go from x to 2^n + y, where y < 2^n (and hence x is), we first want to go to 2^n, because there are no trains that skip over 2^n that do not also skip over 2^n+y < 2^{n+1}.
So any set of stops between x and y must stop at 2^n.
Thus the optimal number of stops from x to 2^n + y is the number of stops from x to 2^n, followed by the number of stops from 2^n to 2^n+y, inclusive (or from 0 to y, which is the same).
The algorithm I propose to get from 0 to y is to start with the high order bit set, and take the train that gets you there, then go on down the list.
Claim: In order to generate a number with k 1s, you must take at least k trains. As proof, if you take a train and it doesn't cause a carry in your stop number, it sets 1 bit. If you take a train and it does cause a carry, the resulting number has at most 1 more set bit than it started with.
To get from x to 2^n is a bit trickier, but can be made simple by tracking the trains you take backwards.
Mapping s_i to s_{2^n-i} and reversing the train steps, any solution for getting from x to 2^n describes a solution for getting from 0 to 2^n-x. And any solution that is optimal for the forward one is optimal for the backward one, and vice versa.
Using the result for getting from 0 to y, we then get that the optimal route from a to b where b highest bit set is 2^n and a does not have that bit set is #b-2^n + #2^n-a, where # means "the number of bits set in the binary representation". And in general, if a and b have a common prefix, simply drop that common prefix.
A local rule that generates the above number of steps is "take the fastest train in your current location that doesn't overshoot your destination".
For the part going from 2^n to 2^n+y we did that explicitly in our proof above. For the part going from x to 2^n this is trickier to see.
First, if the low order bit of x is set, obviously we have to take the first and only train we can take.
Second, imagine x has some collection of unset low-order bits, say m of them. If we played the train game going from x/2^m to 2^(n-m), then scaled the stop numbers by multiplying by 2^m we'd get a solution to going from x to 2^n.
And #(2^n-x)/2^m = #2^n - x. So this "scaled" solution is optimal.
From this, we are always taking the train corresponding to our low-order set bit in this optimal solution. This is the longest range train available, and it doesn't overshoot 2^n.
QED
This problem doesn't require dynamic programming.
Here is a simple implementation of a solution using GCC:
uint32_t min_stops(uint32_t start, uint32_t end)
{
uint32_t stops = 0;
if(start != 0) {
while(start <= end - (1U << __builtin_ctz(start))) {
start += 1U << __builtin_ctz(start);
++stops;
}
}
stops += __builtin_popcount(end ^ start);
return stops;
}
The train schema is a map of powers-of-two. If you visualize the train lines as a bit representation, you can see that the lowest bit set represents the train line with the longest distance between stops that you can take. You can also take the lines with shorter distances.
To minimize the distance, you want to take the line with the longest distance possible, until that would make the end station unreachable. That's what adding by the lowest-set bit in the code does. Once you do this, some number of the upper bits will agree with the upper bits of the end station, while the lower bits will be zero.
At that point, it's simply a a matter of taking a train for the highest bit in the end station that is not set in the current station. This is optimized as __builtin_popcount in the code.
An example going from 5 to 39:
000101 5 // Start
000110 5+1=6
001000 6+2=8
010000 8+8=16
100000 16+16=32 // 32+32 > 39, so start reversing the process
100100 32+4=36 // Optimized with __builtin_popcount in code
100110 36+2=38 // Optimized with __builtin_popcount in code
100111 38+1=39 // Optimized with __builtin_popcount in code
As some have pointed out, since stops are all multiples of powers of 2, trains that stop more frequently also stop at the same stops of the more-express trains. Any stop is on the first train's route, which stops at every station. Any stop is at most 1 unit away from the second train's route, stopping every second station. Any stop is at most 3 units from the third train that stops every fourth station, and so on.
So start at the end and trace your route back in time - hop on the nearest multiple-of-power-of-2 train and keep switching to the highest multiple-of-power-of-2 train you can as soon as possible (check the position of the least significant set bit - why? multiples of powers of 2 can be divided by two, that is bit-shifted right, without leaving a remainder, log 2 times, or as many leading zeros in the bit-representation), as long as its interval wouldn't miss the starting point after one stop. When the latter is the case, perform the reverse switch, hopping on the next lower multiple-of-power-of-2 train and stay on it until its interval wouldn't miss the starting point after one stop, and so on.
We can figure this out doing nothing but a little counting and array manipulation. Like all the previous answers, we need to start by converting both numbers to binary and padding them to the same length. So 12 and 38 become 01100 and 10110.
Looking at station 12, looking at the least significant set bit (in this case the only bit, 2^2) all trains with intervals larger than 2^2 won't stop at station 4, and all with intervals less than or equal to 2^2 will stop at station 4, but will require multiple stops to get to the same destination as the interval 4 train. We in every situation, up until we reach the largest set bit in the end value, we need to take the train with the interval of the least significant bit of the current station.
If we are at station 0010110100, our sequence will be:
0010110100 2^2
0010111000 2^3
0011000000 2^6
0100000000 2^7
1000000000
Here we can eliminate all bits smaller than the lest significant set bit and get the same count.
00101101 2^0
00101110 2^1
00110000 2^4
01000000 2^6
10000000
Trimming the ends at each stage, we get this:
00101101 2^0
0010111 2^0
0011 2^0
01 2^0
1
This could equally be described as the process of flipping all the 0 bits. Which brings us to the first half of the algorithm: Count the unset bits in the zero padded start number greater than the least significant set bit, or 1 if the start station is 0.
This will get us to the only intermediate station reachable by the train with the largest interval smaller than the end station, so all trains after this must be smaller than the previous train.
Now we need to get from station to 100101, it is easier and obvious, take the train with an interval equal to the largest significant bit set in the destination and not set in the current station number.
1000000000 2^7
1010000000 2^5
1010100000 2^4
1010110000 2^2
1010110100
Similar to the first method, we can trim the most significant bit which will always be set, then count the remaining 1's in the answer. So the second part of the algorithm is Count all the set significant bits smaller than the most significant bit
Then Add the result from parts 1 and 2
Adjusting the algorithm slightly to get all the train intervals, here is an example written in javascript so it can be run here.
function calculateStops(start, end) {
var result = {
start: start,
end: end,
count: 0,
trains: [],
reverse: false
};
// If equal there are 0 stops
if (start === end) return result;
// If start is greater than end, reverse the values and
// add note to reverse the results
if (start > end) {
start = result.end;
end = result.start;
result.reverse = true;
}
// Convert start and end values to array of binary bits
// with the exponent matched to the index of the array
start = (start >>> 0).toString(2).split('').reverse();
end = (end >>> 0).toString(2).split('').reverse();
// We can trim off any matching significant digits
// The stop pattern for 10 to 13 is the same as
// the stop pattern for 2 to 5 offset by 8
while (start[end.length-1] === end[end.length-1]) {
start.pop();
end.pop();
}
// Trim off the most sigificant bit of the end,
// we don't need it
end.pop();
// Front fill zeros on the starting value
// to make the counting easier
while (start.length < end.length) {
start.push('0');
}
// We can break the algorithm in half
// getting from the start value to the form
// 10...0 with only 1 bit set and then getting
// from that point to the end.
var index;
var trains = [];
var expected = '1';
// Now we loop through the digits on the end
// any 1 we find can be added to a temporary array
for (index in end) {
if (end[index] === expected){
result.count++;
trains.push(Math.pow(2, index));
};
}
// if the start value is 0, we can get to the
// intermediate step in one trip, so we can
// just set this to 1, checking both start and
// end because they can be reversed
if (result.start == 0 || result.end == 0) {
index++
result.count++;
result.trains.push(Math.pow(2, index));
// We need to find the first '1' digit, then all
// subsequent 0 digits, as these are the ones we
// need to flip
} else {
for (index in start) {
if (start[index] === expected){
result.count++;
result.trains.push(Math.pow(2, index));
expected = '0';
}
}
}
// add the second set to the first set, reversing
// it to get them in the right order.
result.trains = result.trains.concat(trains.reverse());
// Reverse the stop list if the trip is reversed
if (result.reverse) result.trains = result.trains.reverse();
return result;
}
$(document).ready(function () {
$("#submit").click(function () {
var trains = calculateStops(
parseInt($("#start").val()),
parseInt($("#end").val())
);
$("#out").html(trains.count);
var current = trains.start;
var stopDetails = 'Starting at station ' + current + '<br/>';
for (index in trains.trains) {
current = trains.reverse ? current - trains.trains[index] : current + trains.trains[index];
stopDetails = stopDetails + 'Take train with interval ' + trains.trains[index] + ' to station ' + current + '<br/>';
}
$("#stops").html(stopDetails);
});
});
label {
display: inline-block;
width: 50px;
}
<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>
<label>Start</label> <input id="start" type="number" /> <br>
<label>End</label> <input id="end" type="number" /> <br>
<button id="submit">Submit</button>
<p>Shortest route contains <span id="out">0</span> stops</p>
<p id="stops"></p>
Simple Java solution
public static int minimumNumberOfStops(int start, final int end) {
// I would initialize it with 0 but the example given in the question states :
// the minimum number of stops between start = 1 and end = 4 is 3 because we can get from 1 to 2 to 4
int stops = 1;
while (start < end) {
start += findClosestPowerOfTwoLessOrEqualThan(end - start);
stops++;
}
return stops;
}
private static int findClosestPowerOfTwoLessOrEqualThan(final int i) {
if (i > 1) {
return 2 << (30 - Integer.numberOfLeadingZeros(i));
}
return 1;
}
NOTICE: Reason for current comments under my answer is that first I wrote this algorithm completely wrong and user2357112 awared me from my mistakes. So I completely removed that algorithm and wrote a new one according to what user2357112 answered to this question. I also added some comments into this algorithm to clarify what happens in each line.
This algorithm starts at procedure main(Origin, Dest) and it simulate our movements toward destination with updateOrigin(Origin, Dest)
procedure main(Origin, Dest){
//at the end we have number of minimum steps in this variable
counter = 0;
while(Origin != Dest){
//we simulate our movement toward destination with this
Origin = updateOrigin(Origin, Dest);
counter = counter + 1;
}
}
procedure updateOrigin(Origin, Dest){
if (Origin == 1) return 2;
//we must find which train pass from our origin, what comes out from this IF clause is NOT exact choice and we still have to do some calculation in future
if (Origin == 0){
//all trains pass from stop 0, thus we can choose our train according to destination
n = Log2(Dest);
}else{
//its a good starting point to check if it pass from our origin
n = Log2(Origin);
}
//now lets choose exact train which pass from origin and doesn't overshoot destination
counter = 0;
do {
temp = counter * 2 ^ (n - 1);
//we have found suitable train
if (temp == Origin){
//where we have moved to
return Origin + 2 ^ ( n - 1 );
//we still don't know if this train pass from our origin
} elseif (temp < Origin){
counter = counter + 1;
//lets check another train
} else {
n = n - 1;
counter = 0;
}
}while(temp < origin)
}
This grade 11 problem has been bothering me since 2010 and I still can't figure out/find a solution even after university.
Problem Description
There is a very unusual street in your neighbourhood. This street
forms a perfect circle, and the circumference of the circle is
1,000,000. There are H (1 ≤ H ≤ 1000) houses on the street. The
address of each house is the clockwise arc-length from the
northern-most point of the circle. The address of the house at the
northern-most point of the circle is 0. You also have special firehoses
which follow the curve of the street. However, you wish to keep the
length of the longest hose you require to a minimum. Your task is to
place k (1 ≤ k ≤ 1000) fire hydrants on this street so that the maximum
length of hose required to connect a house to a fire hydrant is as
small as possible.
Input Specification
The first line of input will be an integer H, the number of houses. The
next H lines each contain one integer, which is the address of that
particular house, and each house address is at least 0 and less than
1,000,000. On the H + 2nd line is the number k, which is the number of
fire hydrants that can be placed around the circle. Note that a fire
hydrant can be placed at the same position as a house. You may assume
that no two houses are at the same address. Note: at least 40% of the
marks for this question have H ≤ 10.
Output Specification
On one line, output the length of hose required
so that every house can connect to its nearest fire hydrant with that
length of hose.
Sample Input
4
0
67000
68000
77000
2
Output for Sample Input
5000
Link to original question
I can't even come up with a brutal force algorithm since the placement might be float number. For example if the houses are located in 1 and 2, then the hydro should be placed at 1.5 and the distance would be 0.5
Here is quick outline of an answer.
First write a function that can figures out whether you can cover all of the houses with a given maximum length per hydrant. (The maximum hose will be half that length.) It just starts at a house, covers all of the houses it can, jumps to the next, and ditto, and sees whether you stretch. If you fail it tries starting at the next house instead until it has gone around the circle. This will be a O(n^2) function.
Second create a sorted list of the pairwise distances between houses. (You have to consider it going both ways around for a single hydrant, you can only worry about the shorter way if you have 2+ hydrants.) The length covered by a hydrant will be one of those. This takes O(n^2 log(n)).
Now do a binary search to find the shortest length that can cover all of the houses. This will require O(log(n)) calls to the O(n^2) function that you wrote in the first step.
The end result is a O(n^2 log(n)) algorithm.
And here is working code for all but the parsing logic.
#! /usr/bin/env python
def _find_hoses_needed (circle_length, hose_span, houses):
# We assume that houses is sorted.
answers = [] # We can always get away with one hydrant per house.
for start in range(len(houses)):
needed = 1
last_begin = start
current_house = start + 1 if start + 1 < len(houses) else 0
while current_house != start:
pos_begin = houses[last_begin]
pos_end = houses[current_house]
length = pos_end - pos_begin if pos_begin <= pos_end else circle_length + pos_begin - pos_end
if hose_span < length:
# We need a new hose.
needed = needed + 1
last_begin = current_house
current_house = current_house + 1
if len(houses) <= current_house:
# We looped around the circle.
current_house = 0
answers.append(needed)
return min(answers)
def find_min_hose_coverage (circle_length, hydrant_count, houses):
houses = sorted(houses)
# First we find all of the possible answers.
is_length = set()
for i in range(len(houses)):
for j in range(i, len(houses)):
is_length.add(houses[j] - houses[i])
is_length.add(houses[i] - houses[j] + circle_length)
possible_answers = sorted(is_length)
# Now we do a binary search.
lower = 0
upper = len(possible_answers) - 1
while lower < upper:
mid = (lower + upper) / 2 # Note, we lose the fraction here.
if hydrant_count < _find_hoses_needed(circle_length, possible_answers[mid], houses):
# We need a strictly longer coverage to make it.
lower = mid + 1
else:
# Longer is not needed
upper = mid
return possible_answers[lower]
print(find_min_hose_coverage(1000000, 2, [0, 67000, 68000, 77000])/2.0)
Card Trick is a problem on Sphere online judge.
It states that
The magician shuffles a small pack of cards, holds it face down and performs the following procedure:
The top card is moved to the bottom of the pack. The new top card is dealt face up onto the table. It is the Ace of Spades.
Two cards are moved one at a time from the top to the bottom. The next card is dealt face up onto the table. It is the Two of Spades.
Three cards are moved one at a time…
This goes on until the nth and last card turns out to be the n of Spades.
This impressive trick works if the magician knows how to arrange the cards beforehand (and knows how to give a false shuffle). Your program has to determine the initial order of the cards for a given number of cards, 1 ≤ n ≤ 20000.
Input
On the first line of the input is a single positive integer, telling the number of test cases to follow. Each case consists of one line containing the integer n.
Output
For each test case, output a line with the correct permutation of the values 1 to n, space separated. The first number showing the top card of the pack, etc…
Example
Input:
2
4
5
Output:
2 1 4 3
3 1 4 5 2
Now the only solution I can think of is to use a queue and simulate the process.
But that would be O(n^2). I read the comments and they suggested using segment tree of BIT.
I know both segment tree and BIT but am unable to understand how to implement them in this question.
Please suggest some way to do this.
I have no idea why this problem should be linked with BIT or segment tree, but I solved the problem using simple "O(N^2)" simulation.
First the time limit for this problem is 11s, and N == 20000. This indicates that a O(kN) solution may pass the problem. I believe you think this k should be N, because simple simulation requires this, but somehow it can be optimized.
Let's see how we can construct the sequence when N == 5:
Round 1, count 1 space starting from first space after last position: _ 1 _ _ _
Round 2, count 2 spaces starting from first space after last position: _ 1 _ _ 2
Round 3, count 3 spaces starting from first space after last position: 3 1 _ _ 2
Round 4, count 4 spaces starting from first space after last position: 3 1 4 _ 2
Round 5, count 5 spaces starting from first space after last position: 3 1 4 5 2
We can see a nice pattern: for round i, we should count i space starting from the first space after last position, and warp back when necessary.
However, a crucial step is: after some rounds, the spaces left will be smaller than the space to count. In this case, we can take a mod to save time!
For example, in Round 4 of the previous example, we have only 2 spaces left but 4 spaces to count. If we count 4, it's a waste of time. Counting 4 steps is equivalent to count 4 % 2 == 0 space starting from the first space after last position. You can verify this point by yourself :)
Therefore, we can simulate this process using the code:
memset(ans, 255, sizeof(ans));
while (cur <= n)
{
int i, cnt;
int left = n - cur + 1; // count how many spaces left
left = cur % left + 1; // this line is critical, mod to save time!
for (i = pos, cnt = 0; ; ++i) // simulate the process
{
if (i > n) i = 1;
if (ans[i] == -1) ++cnt;
if (cnt == left) break;
}
ans[i] = cur;
pos = i;
++cur;
}
If you want to use a Fenwick tree (BIT) to solve this problem, take a closer look at the solution that nevets posted, particularly this part (thanks for the drawing nevets):
Round 1, count 1 space starting from first space after last position: _ 1 _ _ _
Round 2, count 2 spaces starting from first space after last position: _ 1 _ _ 2
Round 3, count 3 spaces starting from first space after last position: 3 1 _ _ 2
Round 4, count 4 spaces starting from first space after last position: 3 1 4 _ 2
Round 5, count 5 spaces starting from first space after last position: 3 1 4 5 2
Finding the correct free space using the above approach has a time complexity of O(N) because we have to go thru all the spaces (total complexity O(N^2)). Notice that we can calculate the next position using:
free(next_pos) = (free(current_pos) + next_number) mod free(total) + 1
where free(x) tells us how many free spaces are up to (including) a position. This is not a direct formula for next_pos, but it tells us what it needs to satisfy, so we can use this information to binary search it.
The only thing left to do is to do the free space calculations, and this is where BIT comes into play as it gives us a time complexity of O(log N) for both query and for update. The time complexity of finding a free space is now O(log^2 N) and the total time complexity is O(N log^2 N).
As for the running speed:
3.16s for the approach nevets suggested
1.18s using a queue to rotate the elements
0.60s using a linked list to rotate
0.02s using a BIT.
I must say I was quite surprised by the speed gain :-)
P.S. If you're not sure how to use the BIT, initialise by updating all values by +1. When marking a slot as taken, just update it by -1, that's it.
We can solve this also using an indexed set (normal set but with the ability to reach elements in it by indexes like the arrays and vectors)
We can consider this as a faster implementation for the approach of #nevets
and time complexity will be O(NlogN).
Instead of looping throw all elements to find the correct free space. We will store all the free spaces in the indexed set. and every time we take a free space we erase it from the set. and every time we want to find new correct free space we can find it in O(1)
typedef tree<int,null_type,less<int>,rb_tree_tag,tree_order_statistics_node_update> indexed_set_int;
int main() {
int n;
cin >> n;
int a[n]
indexed_set_int st;
for(int i = 0; i < n; i++) {
st.insert(i);
}
int ind = 0;
for(int i = 0; i < n; i++) {
ind += i+1;
ind %= st.size(); // We should mod it to the size of the set to avoid going outside the boundry of the set
auto it = st.find_by_order(ind); // This will get the index of the correct posision from the set of free poisions in O(N)
a[*it] = i+1;
st.erase(it); // remove the free space from the set because we already used it.
}
for(int i = 0; i < n; i++) {
cout << a[i] << " ";
}
}
This solution will be at least the same fast as the BIT. I didn't compare the actual speed of both approaches but from the Time complexity, they both are fast.
Alice and Bob play the following game:
1) They choose a permutation of the first N numbers to begin with.
2) They play alternately and Alice plays first.
3) In a turn, they can remove any one remaining number from the permutation.
4) The game ends when the remaining numbers form an increasing sequence. The person who played the last turn (after which the sequence becomes increasing) wins the game.
Assuming both play optimally, who wins the game?
Input:
The first line contains the number of test cases T. T test cases follow. Each case contains an integer N on the first line, followed by a permutation of the integers 1..N on the second line.
Output:
Output T lines, one for each test case, containing "Alice" if Alice wins the game and "Bob" otherwise.
Sample Input:
2
3
1 3 2
5
5 3 2 1 4
Sample Output:
Alice
Bob
Constraints:
1 <= T <= 100
2 <= N <= 15
The permutation will not be an increasing sequence initially.
I am trying to solve above problem. I have derived till far but I am stuck at a point. Please help me to proceed further.
In above problem, for permutation of length 2, player 1 always wins.
For a permutation of length 3, player 2 wins if the string is strictly increasing or decreasing.
For a permutation of length 4, If player 1 is able to make the string strictly increasing or decreasing by removing a character, she wins else player 2 wins.
Hence a conclusion is:
If current player is able to make the string strictly increasing he/she wins. (Trivial case)
If he/she is able to make it strictly decreasing the the winner is decided by the number of elements in that sequence. If there are even number of elements in that sequence, current player looses, else wins.
But what should be done if the resultant string is neither increasing nor decreasing??
This is a typical game problem. You have 2^15 possible positions which denote which are the remaining numbers. From the number of the remaining numbers you can derive whose turn it is. So now you have a graph that is defined in the following manner - the vertices are the possible sets of remaining numbers and there is an edge connecting two vertices u and v iff there is a move that changes set u to set v(i.e. set v has exactly one number less).
Now you already pointed out for which positions you know who is the winner straight away - the ones that represent increasing sequences of numbers this positions are marked as loosing. For all other positions you determine if they are wining or loosing in the following manner: a position is winning iff there is an edge connecting it to a loosing position. So all that is left is to something like a dfs with memoization and you can determine which positions are winning and which are loosing. As the graph is relatively small (2^15 vertices) this solution should be fast enough.
Hope this helps.
Of course, this can be done with "brute force" for small N, but don't you suspect an easier answer around inversions and the sign of a permutation?
Originally I suspected an answer like "Alice wins iff the sign is -1, else loses", but this is not the case.
But I would like to propose a representation of the problem that not only your algorithm may use, but one that will equally boost your paper-and-pen performance in this game.
An inversion is a pair of indices i<j such that a[i]>a[j]. Consider (i,j) an edge of an undirected graph with vertices 1,...,N. Each player deletes a vertex from this graph and wins if there are no edges left.
For 5 3 2 1 4, the resulting graph is
5--3
/|\ |
/ | \|
4 1--2
and Alice quickly sees that removing "5" gives Bob the opportunity to remove 2. Then no inversions are left, and Bob wins.
This game can be solved recursively.
Each time alice takes her first pick and picks i, subtract 1 from all the remaining numbers that are larger than i. Now we have the same game but with the numbers 1 to N-1
lets say your sequence is
1,3,5,4,2
on her first move, Alice can pick any number.
case1:
she picks 1, alice can win if bob cant win with 3,5,4,2 (equivalently 2,4,3,1)
case2:
she picks 3 first. Alice can win if bob cant win with 1,5,4,2 (equivalently 1,4,3,2)
case3:
she picks 5 first. Alice can win if bob cant win with 1,3,4,2
you get the idea.
So you can make a recursive function to work out the solution for a size N permutation all by using size N-1 permutations for each possible first guess. the base case for the recursion is when you have an in-order sequence.
Each step of the recursion, the person tries all possibilities and picks any that makes them win.
Because there are many combinations of moves that can get down to the same sequence, the recursion has overlapping sub problems. This means we can use dynamic programming, or simply "memoize" our function, greatly increasing efficiency.
For further speedup one may use symmetry in the permutations, as many groups of permutations are equivalent, such as the reverse of one permutation would yield the same result.
Good luck.
#tiwo ,#rup COnsidering 5 3 2 1 4 is the sequence first alice removes 5 and the bob removes 2 then the sequence is 3 1 4 which is not in increasing order then alice gets the chance to remove 1 and the the sequence is in ascending order Alice should be the answer. In the graph you gave there should be an edge between 3 and 1 as 1 and 3 are in inversion.
Please tell me where i am wrong as the answer given in the problem is infact BOB
You can solve it with minimax algorithm. Here is the code in java
import java.io.*;
import java.util.*;
import java.text.*;
import java.math.*;
import java.util.regex.*;
public class Solution {
public static Scanner sc = new Scanner(System.in);
public static void main(String[] args) {
int t = ni();
for(int i=0; i<t; i++){
int n = ni();
Map<Long, Boolean> map = new HashMap<Long, Boolean>();
int[] numbers = new int[n];
for(int j=0; j<n; j++){
numbers[j] = ni();
}
if(aliceWin(numbers, map)) System.out.println("Alice");
else System.out.println("Bob");
}
}
public static boolean aliceWin(int[] a, Map<Long, Boolean> map){
long h = hashCode(a); int temp;
if(map.containsKey(h)) return true;
for(int i=0; i<a.length; i++){
if(a[i]>0){
temp = a[i] ;
a[i] = 0;
if(isIncreasing(a)){
map.put(h, true);
a[i] = temp;
return true;
}
if(!aliceWin(a, map)) {
map.put(h, true);
a[i] = temp;
return true;
}
a[i] = temp;
}
}
return false;
}
public static long hashCode(int[] a){
long result = 0;
for(int i=0; i<a.length; i++){
result = (result << 4) + a[i];
}
return result;
}
public static boolean isIncreasing(int[] a){
int last = 0;
for(int i=0; i<a.length; i++){
if (a[i] > 0){
if(last > a[i]) return false;
last = a[i];
}
}
return true;
}
public static int ni(){
return sc.nextInt();
}
public static void print(Object... args){
System.out.println(Arrays.deepToString(args));
}
}
From blog: hackerrank-permutation-game
Here is some code that builds the graph for you, but requires you to call reverse() on the graph, create a source node connecting to all nodes in the base, flow back to source seeing if there is a way alice wins.
input_ = """2
3
1 3 2
5
5 3 2 1 4""".splitlines()
perms = [map(int,perm.split()) for perm in input_ if len(perm)>1]
"[['1', '3', '2'], ['5', '3', '2', '1', '4']]"
if networkx is None:
import networkx
from itertools import combinations
def build_graph(perm):
base = set()
G = networkx.DiGraph()
for r in range(1,len(perm)+1):
for combo in combinations(perm,r):
combo = list(combo)
if combo == sorted(combo):
base.add(tuple(combo))
continue
for i in range(r):
G.add_edge(tuple(combo),tuple(combo[:i]+combo[i+1:])) #you may want to reverse the graph later to point from base to source.
return G,base
def solve(G,base):
#dfs,
pass
for perm in perms:
G,base = build_graph(perms[0])
print solve(G,base)
can't we just check at each step that..
does a single change by the next player makes the sequence sorted.. if yes then make some other move..
or carry on with the move
like
5 3 2 1 4
if alice does 3 2 1 4
bob cannot win in a single turn by eliminating any...
like if
he does 2 1 4 it is nt sorted..
he does 3 1 4 it is nt sorted..
he does 3 2 4 it is nt sorted..
so 5 3 2 1 4 -> 3 2 1 4 is a valid move!!
now is bob's turn..
he'll check the same..
but in some time..there won't be a number such that u can make a move as above..
so u'll have to make a random move and who will win then can be easily calculated by the number of steps tht will make the sequence into single element!!
To me (using almost your own words):
If he/she is able to make it strictly increasing on the first move he/she wins (Trivial case) otherwise the the winner is decided by the number of elements in that sequence.
Take your second case as example.
I think that the graph solution is fine but it forgets that the players play in a optimal way. So don't need to check all the different path since some of them will derive from a non-optimal choice.