How can I approach this CP task? - c++

Question

The task (from a Bulgarian judge, click on "Език" to change it to English):
I am given the size of the first (S1 = A) of N corals. The size of every subsequent coral (Si, where i > 1) is calculated using the formula (B*Si-1 + C)%D, where A, B, C and D are some constants. I am told that Nemo is nearby the Kth coral (when the sizes of all corals are sorted in ascending order).
What is the size of the above-mentioned Kth coral ?
I will have T tests and for every one of them I will be given N, K, A, B, C and D and prompted to output the size of the Kth coral.
The requirements:
1 ≤ T ≤ 3
1 ≤ K ≤ N ≤ 107
0 ≤ A < D ≤ 1018
1 ≤ C, B*D ≤ 1018
Memory available is 64 MB
Time limit is 1.9 sec
The problem I have:
For the worst case scenario I will need 107*8B which is 76 MB.
The solution If the memory available was at least 80 MB would be:
#include <iostream>
#include <vector>
#include <iterator>
#include <algorithm>
using biggie = long long;
int main() {
int t;
std::cin >> t;
int i, n, k, j;
biggie a, b, c, d;
std::vector<biggie>::iterator it_ans;
for (i = 0; i != t; ++i) {
std::cin >> n >> k >> a >> b >> c >> d;
std::vector<biggie> lut{ a };
lut.reserve(n);
for (j = 1; j != n; ++j) {
lut.emplace_back((b * lut.back() + c) % d);
}
it_ans = std::next(lut.begin(), k - 1);
std::nth_element(lut.begin(), it_ans, lut.end());
std::cout << *it_ans << '\n';
}
return 0;
}
Question 1: How can I approach this CP task given the requirements listed above ?
Question 2: Is it somehow possible to use std::nth_element to solve it since I am not able to store all N elements ? I mean using std::nth_element in a sliding window technique (If this is possible).
# Christian Sloper
#include <iostream>
#include <queue>
using biggie = long long;
int main() {
int t;
std::cin >> t;
int i, n, k, j, j_lim;
biggie a, b, c, d, prev, curr;
for (i = 0; i != t; ++i) {
std::cin >> n >> k >> a >> b >> c >> d;
if (k < n - k + 1) {
std::priority_queue<biggie, std::vector<biggie>, std::less<biggie>> q;
q.push(a);
prev = a;
for (j = 1; j != k; ++j) {
curr = (b * prev + c) % d;
q.push(curr);
prev = curr;
}
for (; j != n; ++j) {
curr = (b * prev + c) % d;
if (curr < q.top()) {
q.pop();
q.push(curr);
}
prev = curr;
}
std::cout << q.top() << '\n';
}
else {
std::priority_queue<biggie, std::vector<biggie>, std::greater<biggie>> q;
q.push(a);
prev = a;
for (j = 1, j_lim = n - k + 1; j != j_lim; ++j) {
curr = (b * prev + c) % d;
q.push(curr);
prev = curr;
}
for (; j != n; ++j) {
curr = (b * prev + c) % d;
if (curr > q.top()) {
q.pop();
q.push(curr);
}
prev = curr;
}
std::cout << q.top() << '\n';
}
}
return 0;
}

Answer 1

This gets accepted (Succeeds all 40 tests. Largest time 1.4 seconds, for a test with T=3 and D≤10^9. Largest time for a test with larger D (and thus T=1) is 0.7 seconds.).
#include <iostream>
using biggie = long long;
int main() {
int t;
std::cin >> t;
int i, n, k, j;
biggie a, b, c, d;
for (i = 0; i != t; ++i) {
std::cin >> n >> k >> a >> b >> c >> d;
biggie prefix = 0;
for (int shift = d > 1000000000 ? 40 : 20; shift >= 0; shift -= 20) {
biggie prefix_mask = ((biggie(1) << (40 - shift)) - 1) << (shift + 20);
int count[1 << 20] = {0};
biggie s = a;
int rank = 0;
for (j = 0; j != n; ++j) {
biggie s_vs_prefix = s & prefix_mask;
if (s_vs_prefix < prefix)
++rank;
else if (s_vs_prefix == prefix)
++count[(s >> shift) & ((1 << 20) - 1)];
s = (b * s + c) % d;
}
int i = -1;
while (rank < k)
rank += count[++i];
prefix |= biggie(i) << shift;
}
std::cout << prefix << '\n';
}
return 0;
}
The result is a 60 bits number. I first determine the high 20 bits with one pass through the numbers, then the middle 20 bits in another pass, then the low 20 bits in another.
For the high 20 bits, generate all the numbers and count how often each high 20 bits pattern occurrs. After that, add up the counts until you reach K. The pattern where you reach K, that pattern covers the K-th largest number. In other words, that's the result's high 20 bits.
The middle and low 20 bits are computed similarly, except we take the by then known prefix (the high 20 bits or high+middle 40 bits) into account. As a little optimization, when D is small, I skip computing the high 20 bits. That got me from 2.1 seconds down to 1.4 seconds.
This solution is like user3386109 described, except with bucket size 2^20 instead of 10^6 so I can use bit operations instead of divisions and think of bit patterns instead of ranges.

Answer 2

For the memory constraint you hit:
(B*Si-1 + C)%D
requires only the value (Si-2) before itself. So you can compute them in pairs, to use only 1/2 of total you need. This only needs indexing even values and iterating once for odd values. So you can just use half-length LUT and compute the odd value in-flight. Modern CPUs are fast enough to do extra calculations like these.
std::vector<biggie> lut{ a_i,a_i_2,a_i_4,... };
a_i_3=computeOddFromEven(lut[1]);
You can make a longer stride like 4,8 too. If dataset is large, RAM latency is big. So it's like having checkpoints in whole data search space to balance between memory and core usage. 1000-distance checkpoints would put a lot of cpu cycles into re-calculations but then the array would fit CPU's L2/L1 cache which is not bad. When sorting, the maximum re-calc iteration per element would be n=1000 now. O(1000 x size) maybe it's a big constant but maybe somehow optimizable by compiler if some constants really const?
If CPU performance becomes problem again:
write a compiling function that writes your source code with all the "constant" given by user to a string
compile the code using command-line (assuming target computer has some accessible from command line like g++ from main program)
run it and get results
Compiler should enable more speed/memory optimizations when those are really constant in compile-time rather than depending on std::cin.
If you really need to add a hard-limit to the RAM usage, then implement a simple cache with the backing-store as your heavy computations with brute-force O(N^2) (or O(L x N) with checkpoints every L elements as in first method where L=2 or 4, or ...).
Here's a sample direct-mapped cache with 8M long-long value space:
int main()
{
std::vector<long long> checkpoints = {
a_0, a_16, a_32,...
};
auto cacheReadMissFunction = [&](int key){
// your pure computational algorithm here, helper meant to show variable
long long result = checkpoints[key/16];
for(key - key%16 times)
result = iterate(result);
return result;
};
auto cacheWriteMissFunction = [&](int key, long long value){
/* not useful for your algorithm as it doesn't change behavior per element */
// backing_store[key] = value;
};
// due to special optimizations, size has to be 2^k
int cacheSize = 1024*1024*8;
DirectMappedCache<int, long long> cache(cacheSize,cacheReadMissFunction,cacheWriteMissFunction);
std::cout << cache.get(20)<<std::endl;
return 0;
}
If you use a cache-friendly sorting-algorithm, a direct cache access would make a lot of re-use for nearly all the elements in comparisons if you fill the output buffer/terminal with elements one by one by following something like a bitonic-sort-path (that is known in compile-time). If that doesn't work, then you can try accessing files as a "backing-store" of cache for sorting whole array at once. Is file system prohibited for use? Then the online-compiling method above won't work either.
Implementation of a direct mapped cache (don't forget to call flush() after your algorithm finishes, if you use any cache.set() method):
#ifndef DIRECTMAPPEDCACHE_H_
#define DIRECTMAPPEDCACHE_H_
#include<vector>
#include<functional>
#include<mutex>
#include<iostream>
/* Direct-mapped cache implementation
* Only usable for integer type keys in range [0,maxPositive-1]
*
* CacheKey: type of key (only integers: int, char, size_t)
* CacheValue: type of value that is bound to key (same as above)
*/
template< typename CacheKey, typename CacheValue>
class DirectMappedCache
{
public:
// allocates buffers for numElements number of cache slots/lanes
// readMiss: cache-miss for read operations. User needs to give this function
// to let the cache automatically get data from backing-store
// example: [&](MyClass key){ return redis.get(key); }
// takes a CacheKey as key, returns CacheValue as value
// writeMiss: cache-miss for write operations. User needs to give this function
// to let the cache automatically set data to backing-store
// example: [&](MyClass key, MyAnotherClass value){ redis.set(key,value); }
// takes a CacheKey as key and CacheValue as value
// numElements: has to be integer-power of 2 (e.g. 2,4,8,16,...)
DirectMappedCache(CacheKey numElements,
const std::function<CacheValue(CacheKey)> & readMiss,
const std::function<void(CacheKey,CacheValue)> & writeMiss):size(numElements),sizeM1(numElements-1),loadData(readMiss),saveData(writeMiss)
{
// initialize buffers
for(size_t i=0;i<numElements;i++)
{
valueBuffer.push_back(CacheValue());
isEditedBuffer.push_back(0);
keyBuffer.push_back(CacheKey()-1);// mapping of 0+ allowed
}
}
// get element from cache
// if cache doesn't find it in buffers,
// then cache gets data from backing-store
// then returns the result to user
// then cache is available from RAM on next get/set access with same key
inline
const CacheValue get(const CacheKey & key) noexcept
{
return accessDirect(key,nullptr);
}
// only syntactic difference
inline
const std::vector<CacheValue> getMultiple(const std::vector<CacheKey> & key) noexcept
{
const int n = key.size();
std::vector<CacheValue> result(n);
for(int i=0;i<n;i++)
{
result[i]=accessDirect(key[i],nullptr);
}
return result;
}
// thread-safe but slower version of get()
inline
const CacheValue getThreadSafe(const CacheKey & key) noexcept
{
std::lock_guard<std::mutex> lg(mut);
return accessDirect(key,nullptr);
}
// set element to cache
// if cache doesn't find it in buffers,
// then cache sets data on just cache
// writing to backing-store only happens when
// another access evicts the cache slot containing this key/value
// or when cache is flushed by flush() method
// then returns the given value back
// then cache is available from RAM on next get/set access with same key
inline
void set(const CacheKey & key, const CacheValue & val) noexcept
{
accessDirect(key,&val,1);
}
// thread-safe but slower version of set()
inline
void setThreadSafe(const CacheKey & key, const CacheValue & val) noexcept
{
std::lock_guard<std::mutex> lg(mut);
accessDirect(key,&val,1);
}
// use this before closing the backing-store to store the latest bits of data
void flush()
{
try
{
std::lock_guard<std::mutex> lg(mut);
for (size_t i=0;i<size;i++)
{
if (isEditedBuffer[i] == 1)
{
isEditedBuffer[i]=0;
auto oldKey = keyBuffer[i];
auto oldValue = valueBuffer[i];
saveData(oldKey,oldValue);
}
}
}catch(std::exception &ex){ std::cout<<ex.what()<<std::endl; }
}
// direct mapped access
// opType=0: get
// opType=1: set
CacheValue const accessDirect(const CacheKey & key,const CacheValue * value, const bool opType = 0)
{
// find tag mapped to the key
CacheKey tag = key & sizeM1;
// compare keys
if(keyBuffer[tag] == key)
{
// cache-hit
// "set"
if(opType == 1)
{
isEditedBuffer[tag]=1;
valueBuffer[tag]=*value;
}
// cache hit value
return valueBuffer[tag];
}
else // cache-miss
{
CacheValue oldValue = valueBuffer[tag];
CacheKey oldKey = keyBuffer[tag];
// eviction algorithm start
if(isEditedBuffer[tag] == 1)
{
// if it is "get"
if(opType==0)
{
isEditedBuffer[tag]=0;
}
saveData(oldKey,oldValue);
// "get"
if(opType==0)
{
const CacheValue && loadedData = loadData(key);
valueBuffer[tag]=loadedData;
keyBuffer[tag]=key;
return loadedData;
}
else /* "set" */
{
valueBuffer[tag]=*value;
keyBuffer[tag]=key;
return *value;
}
}
else // not edited
{
// "set"
if(opType == 1)
{
isEditedBuffer[tag]=1;
}
// "get"
if(opType == 0)
{
const CacheValue && loadedData = loadData(key);
valueBuffer[tag]=loadedData;
keyBuffer[tag]=key;
return loadedData;
}
else // "set"
{
valueBuffer[tag]=*value;
keyBuffer[tag]=key;
return *value;
}
}
}
}
private:
const CacheKey size;
const CacheKey sizeM1;
std::mutex mut;
std::vector<CacheValue> valueBuffer;
std::vector<unsigned char> isEditedBuffer;
std::vector<CacheKey> keyBuffer;
const std::function<CacheValue(CacheKey)> loadData;
const std::function<void(CacheKey,CacheValue)> saveData;
};
#endif /* DIRECTMAPPEDCACHE_H_ */

Answer 3

You can solve this problem using a Max-heap.
Insert the first k elements into the max-heap. The largest element of these k will now be at the root.
For each remaining element e:
Compare e to the root.
If e is larger than the root, discard it.
If e is smaller than the root, remove the root and insert e into the heap structure.
After all elements have been processed, the k-th smallest element is at the root.
This method uses O(K) space and O(n log n) time.

Answer 4

There’s an algorithm that people often call LazySelect that I think would be perfect here.
With high probability, we make two passes. In the first pass, we save a random sample of size n much less than N. The answer will be around index (K/N)n in the sorted sample, but due to the randomness, we have to be careful. Save the values a and b at (K/N)n ± r instead, where r is the radius of the window. In the second pass, we save all of the values in [a, b], count the number of values less than a (let it be L), and select the value with index K−L if it’s in the window (otherwise, try again).
The theoretical advice on choosing n and r is fine, but I would be pragmatic here. Choose n so that you use most of the available memory; the bigger the sample, the more informative it is. Choose r fairly large as well, but not quite as aggressively due to the randomness.
C++ code below. On the online judge, it’s faster than Kelly’s (max 1.3 seconds on the T=3 tests, 0.5 on the T=1 tests).
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <iostream>
#include <limits>
#include <optional>
#include <random>
#include <vector>
namespace {
class LazySelector {
public:
static constexpr std::int32_t kTargetSampleSize = 1000;
explicit LazySelector() { sample_.reserve(1000000); }
void BeginFirstPass(const std::int32_t n, const std::int32_t k) {
sample_.clear();
mask_ = n / kTargetSampleSize;
mask_ |= mask_ >> 1;
mask_ |= mask_ >> 2;
mask_ |= mask_ >> 4;
mask_ |= mask_ >> 8;
mask_ |= mask_ >> 16;
}
void FirstPass(const std::int64_t value) {
if ((gen_() & mask_) == 0) {
sample_.push_back(value);
}
}
void BeginSecondPass(const std::int32_t n, const std::int32_t k) {
sample_.push_back(std::numeric_limits<std::int64_t>::min());
sample_.push_back(std::numeric_limits<std::int64_t>::max());
const double p = static_cast<double>(sample_.size()) / n;
const double radius = 2 * std::sqrt(sample_.size());
const auto lower =
sample_.begin() + std::clamp<std::int32_t>(std::floor(p * k - radius),
0, sample_.size() - 1);
const auto upper =
sample_.begin() + std::clamp<std::int32_t>(std::ceil(p * k + radius), 0,
sample_.size() - 1);
std::nth_element(sample_.begin(), upper, sample_.end());
std::nth_element(sample_.begin(), lower, upper);
lower_ = *lower;
upper_ = *upper;
sample_.clear();
less_than_lower_ = 0;
equal_to_lower_ = 0;
equal_to_upper_ = 0;
}
void SecondPass(const std::int64_t value) {
if (value < lower_) {
++less_than_lower_;
} else if (upper_ < value) {
} else if (value == lower_) {
++equal_to_lower_;
} else if (value == upper_) {
++equal_to_upper_;
} else {
sample_.push_back(value);
}
}
std::optional<std::int64_t> Select(std::int32_t k) {
if (k < less_than_lower_) {
return std::nullopt;
}
k -= less_than_lower_;
if (k < equal_to_lower_) {
return lower_;
}
k -= equal_to_lower_;
if (k < sample_.size()) {
const auto kth = sample_.begin() + k;
std::nth_element(sample_.begin(), kth, sample_.end());
return *kth;
}
k -= sample_.size();
if (k < equal_to_upper_) {
return upper_;
}
return std::nullopt;
}
private:
std::default_random_engine gen_;
std::vector<std::int64_t> sample_ = {};
std::int32_t mask_ = 0;
std::int64_t lower_ = std::numeric_limits<std::int64_t>::min();
std::int64_t upper_ = std::numeric_limits<std::int64_t>::max();
std::int32_t less_than_lower_ = 0;
std::int32_t equal_to_lower_ = 0;
std::int32_t equal_to_upper_ = 0;
};
} // namespace
int main() {
int t;
std::cin >> t;
for (int i = t; i > 0; --i) {
std::int32_t n;
std::int32_t k;
std::int64_t a;
std::int64_t b;
std::int64_t c;
std::int64_t d;
std::cin >> n >> k >> a >> b >> c >> d;
std::optional<std::int64_t> ans = std::nullopt;
LazySelector selector;
do {
{
selector.BeginFirstPass(n, k);
std::int64_t s = a;
for (std::int32_t j = n; j > 0; --j) {
selector.FirstPass(s);
s = (b * s + c) % d;
}
}
{
selector.BeginSecondPass(n, k);
std::int64_t s = a;
for (std::int32_t j = n; j > 0; --j) {
selector.SecondPass(s);
s = (b * s + c) % d;
}
}
ans = selector.Select(k - 1);
} while (!ans);
std::cout << *ans << '\n';
}
}