errorgorn's blog

By errorgorn, 7 days ago, In English

Hello, Codeforces!

Welcome to the Codeforces Raif Round 1 (Div. 1 + Div. 2) supported by Raiffeisenbank, that will start on Oct/17/2020 16:05 (Moscow time). It will be a combined rated round for both divisions. Note that the start time is unusual.

All problems were authored and prepared by bensonlzl, oolimry, errorgorn, dvdg6566, shenxy13.

Ari gato to:

You will be given 8 problems, one of which would be divided into easy and hard versions, and 150 minutes to solve them.

We hope that statements are short and pretests are strong and that you find the problems interesting! Good luck, have fun and we wish everyone high ratings!

The scoring distribution will be announced closer to the beginning of the round.

Thanks to Raiffeisenbank, winners will get awesome swag:

  • 1st-3rd place = Bluetooth speaker

  • 4th-10th place = Bumbag

  • 11th-20th place = Power Bank

Random 50 participants outside of top-20, who solved at least one problem will receive:

  • Thermos Mugs

  • Raiffeisenbank t-shirt

About Algorithmic Trading team in Raiffeisenbank

We develop a high-frequency trading (HFT) system for equity, currency and derivative markets. Our business edge is in technology. The main goal is to create a top-notch platform based on fundamental and statistical models and machine learning, with low latency and high throughput. The efficiency and scalability of our code give us a competitive advantage. We are passionate about code quality and strive for highest standards in product development.

If you are interested in internship and employment opportunities in the Raiffeisenbank algo-trading team Capital Markets, or want to get in touch with the bank's recruitment , fill out a form below.


UPD: Scoring distribution: 500 — 1000 — 1000 — 1500 — 1750 — 1750 — (2250+750) — 4000

UPD2: Editorial out!

UPD 3: First ACs and winners

First ACs

A: 300iq

B: icecuber

C: Not-Afraid

D: Radewoosh

E: errichto

F: fmota

G: Radewoosh

H: Radewoosh

Top 5

  1. Radewoosh

  2. Um_nik

  3. kczno1

  4. ecnerwala

  5. littlelittlehorse

Congratulations to everyone!

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By errorgorn, 4 months ago, In English

So my O level Chinese exam is in 2 days so I decided to learn a data structure that I can only find resources for in Chinese. I thought I might as well write a tutorial in English.

This data structure is called 析合树, directly translated is cut join tree, but I think permutation tree is a better name. Honestly, after learning about it, it seems like a very niche data structure with very limited uses, but anyways here is the tutorial on it.

Thanks to dantoh and oolimry for helping me proofread.


Consider this problem. We are given a permutation,$$$P$$$ of length $$$n$$$. A good range is a contiguous subsequence such that $$$\max\limits_{l \leq i \leq r} P_i - \min\limits_{l \leq i \leq r} P_i = r-l$$$. This can be thought of the number of contiguous subsequence such that when we sort the numbers in this subsequence, we get contiguous values. Count the number of good ranges.

Example: $$$P=\{5,3,4,1,2\}$$$.

All good ranges are $$$[1,1], [2,2], [3,3], [4,4], [5,5], [2,3], [4,5], [1,3], [2,5], [1,5]$$$.

The $$$O(n^2)$$$ solution for this is using sparse table and checking every subsequence if it fits the given conditions. But it turns out we can speed this up using permutation tree to $$$O(n\log{n})$$$.


A permutation $$$P$$$ of length $$$n$$$ is defined as:

  • $$$|P|=n$$$
  • $$$\forall i, P_i \in [1,n]$$$
  • $$$\nexists i,j \in [1,n], P_i \ne P_j$$$

A good range is defined as a range, $$$[l,r]$$$ such that $$$\max\limits_{l \leq i \leq r} P_i - \min\limits_{l \leq i \leq r} P_i = r-l$$$ or equivalently $$$\nexists x,z \in [l,r], y \notin [l,r], P_x<P_y<P_z$$$.

We denote a good range $$$[l,r]$$$ of $$$P$$$ as $$$(P, [l,r])$$$, and also denote the set of all good ranges as $$$I_g$$$.

Permutation Tree

So we want a structure that can store all good ranges efficiently.

Firstly, we can notice something about these good ranges. They are composed by the concatenation of other good ranges.

So the structure of the tree is that a node can have some children and the range of the parent is made up of the concatenation of the children's ranges.

Here is an example permutation. $$$P=\{9,1,10,3,2,5,7,6,8,4\}$$$.


As we can see from the above image, every node represents a certain good range, where the values in the node represent the minimum and maximum values contains in this range.

Notice in this data structure, for any 2 nodes $$$[l_1,r_1]$$$ and $$$[l_2,r_2]$$$, WLOG $$$l_1 \leq l_2$$$, either $$$r_1<l_2$$$ or $$$r_2 \leq r_1$$$.

Definition of Cut Nodes and Join Nodes

We shall define some terms used in this data structure:

  • Node range: For some node $$$u$$$, $$$[u_l,u_r]$$$ will describe the minimum and maximum value contained in the range the node represents
  • Ranges of children: For some non-leaf node $$$u$$$, let the array $$$S_u$$$ denote the array of the ranges of its children. For example, the root node the above picture, $$$S_u$$$ is $$$\{[9,9],[1,1],[10,10],[2,8]\}$$$.
  • Order of children: For some non-leaf node $$$u$$$, we can discretize the ranges in $$$S_u$$$. Again using the example of the root node, the order of its children is $$$\{3,1,4,2\}$$$, we will call this $$$D_u$$$.
  • Join node: For some non-leaf node $$$u$$$, we call it a join node if $$$D_u=\{1,2,3,\cdots\}$$$ or $$$D_u=\{\cdots,3,2,1\}$$$. For simplicity we also consider all leaf nodes to be join nodes.
  • Cut node: Any node that is not a join node.

Properties of Cut Nodes and Join Nodes

Firstly, we have this very trivial property. The union of all ranges of children is the node's range. Or in fancy math notation, $$$\bigcup_{i=1}^{|S_u|} S_u[i]=[u_l,u_r]$$$.

For a join node $$$u$$$, any contiguous subsequence of ranges of its children is a good range. Or, $$$\forall i,j,1 \leq i \leq j \leq |S_u|, \bigcup_{i=l}^{r} S_u[i]\in I_g$$$.

For a cut node $$$u$$$, the opposite is true. Any contiguous subsequence of ranges of its children larger than 1 is not a good range. Or, $$$\forall i,j,1 \leq i < j \leq |S_u|, \bigcup_{i=l}^{r} S_u[i]\notin I_g$$$.

The property of join nodes is not too hard to show by looking at the definition of what a join node is.

But the property of cut nodes is slightly harder to prove. A way to think about this is that for some cut node such that there is a subsequence of ranges bigger than 1 that form a good range, then that subsequence would have formed a range. This is a contradiction.

Construction of Permutation Tree

Now we will discuss a method to create the Permutation Tree in $$$O(n\log{n})$$$. According to a comment by CommonAnts, the creator of this data structure, a $$$O(n)$$$ algorithm exists, but I could not find any resources on it.

Brief overview of algorithm

We process the permutation from left to right. We will also keep a stack of cut and join nodes that we have processed previously. Now let us consider adding $$$P_i$$$ to this stack. We firstly make a new node $$$[P_i,P_i]$$$ and call it the node we are currently processing.

  1. Check if we can add the currently processed as a child of the node on top of the stack.
  2. If we cannot, check if we can make a new parent node (this can either be a cut or join node) such that it contains some suffix of the stack and the current processed node as children.
  3. Repeat this process until we cannot do any more operations of type 1 or 2.
  4. Finally, push the currently processed node to the stack.

Notice that after processing all nodes, we will only have 1 node left on the stack, which is the root node.

Details of the algorithm

For operation 1, if we note that we can only do this if the node on top of the stack is a join node. Because if we can add this as a child to a cut node, then it contradicts the fact that no contiguous subsequence of ranges of children larger than 1 of a cut node can be a good range.

For operation 2, we need a fast way to find if there exists a good range such that we can make a new node from. There are 3 cases:

  • We cannot make a new node.
  • We can make a new join node. This new node has 2 children.
  • We can make a new cut node.

Checking if there exists a good range

We have established for a good range $$$(P,[l,r])$$$ that $$$\max\limits_{l \leq i \leq r} P_i - \min\limits_{l \leq i \leq r} P_i = r-l$$$.

Since $$$P$$$ is a permutation, we also have $$$\max\limits_{l \leq i \leq r} P_i - \min\limits_{l \leq i \leq r} P_i \geq r-l$$$ for all ranges $$$[l,r]$$$.

Equivalently, we have $$$\max\limits_{l \leq i \leq r} P_i - \min\limits_{l \leq i \leq r} P_i - (r-l) \geq 0$$$, where we have equality only for good ranges.

Say that we are currently processing $$$P_i$$$. We define a value $$$Q$$$ for each range $$$[j,i], Q_j=\max\limits_{j \leq k \leq i} P_k - \min\limits_{j \leq k \leq i} P_k - (i-j),0< j \leq i$$$. Now we just need to check if there is some $$$Q_j=0$$$, where $$$j$$$ is not in the current node being processed.

Now we only need to know how to maintain this values of $$$Q_j$$$ quickly when we transition from $$$P_i$$$ to $$$P_{i+1}$$$. We can do this by updating the max and min values every time it changes. How can we do this?

Let's focus on updating the max values since updating the min values are similar. Let's consider when the max value will change. It changes every time $$$P_{i+1} > \max $$$. Let us maintain a stack of the values of $$$\max\limits_{j \leq k \leq i}P_k$$$, where we will store distinct values only. It can be seen that this stack is monotonically decreasing. When we add a new element to this stack, we will pop all elements in the stack which are smaller than it and update their maximum values using a segment tree range add update. This amortizes to $$$O(n)$$$ as each value is pushed into the stack once.

Do note to decrement all $$$Q_j$$$ by 1 since we are incrementing $$$i$$$ by 1.

Now that we can maintain all values of $$$Q_j$$$, we can simply check the minimum value of the range we are interested in using segment tree range minimum queries.

If we can make a new cut node, then we greedily try to make new cut node. We can do this by adding another node from our stack until our new cut node is valid.

Since the above may be confusing, here is a illustration of how the construction looks like.


Problems using Permutation Tree

Codeforces 526F – Pudding Monsters


CERC 17 Problem I – Instrinsic Interval


Codeforces 997E – Good Subsegments

Codeforces 1205F – Beauty of a Permutation

CodeChef – Army of Me

CodeChef – Good Subsequences

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By errorgorn, history, 11 months ago, In English

Sumitomo Mitsui Trust Bank Programming Contest 2019 has just finished, and this is an unofficial editorial.

Thanks to my friends limruiy and shenxy13 for helping write some of the editorial.

A — November 30

You can solve it simply by checking for each end date of the Gregorian calendar. However, note that as the second date directly follows the first date (a fact which I think is not clear in the English translation), we can also check whether they're in different months, or whether the second date is the first day of a month. This can be done in constant time.


B — Tax Rate

We note that there is monotonicty in this question. $$$\lfloor 1.08(X+1) \rfloor \geqslant \lfloor 1.08X \rfloor$$$. Hence, we can binary search the answer. When we binary search the value of X(the answer), if $$$\lfloor 1.08X \rfloor = N$$$, then we have our answer. Otherwise, we can search higher if $$$\lfloor 1.08X \rfloor > N$$$ and search lower otherwise. If we find that no number gives us desired N, then it is impossible.


C — 100 to 105

We can simply do a 0-infinity knapsack with weights 100,101,...,105 and check if some value is reachable. We get a time complexity of $$$O(N)$$$.


D — Lucky PIN

First, we note that there are $$$O(N^{3})$$$ subsequences of the string, so generating all of them and using a set to check for number of distinct subsequences is TLE. However, there are only at most 1000 distinct such subsequences, from 000 to 999. We can linearly scan through the string for each of these possible subsequences to check if it is actually a subsequence of the string in $$$O(N)$$$. Thus, this can be solved in $$$O(1000N)$$$, which is AC.


E — Colorful Hats 2

Firstly, we can imagine there are 3 imaginary people standing at the very front, each with a different colour hat. For each person, we consider how many possible people could be the first person in front of him with the same hat colour. If the current person has number X, then the number of ways is:

(no. of ppl with X-1 in front) — (no. of ppl with X in front)

This is because those with X in front of him must be paired with one of the X-1 already, so this reduces our options.

Implementation wise, we can store an array which keeps track of the number of people with no. X who are not paired yet. Initially, all values are 0, except at index -1 with the value of 3. Then when processing the current p[user:AtillaAk]erson X, we multiply the answer by arr[X-1], decrease arr[X-1] by 1, and increase arr[X] by 1.


F — Interval Running

Firstly, if $$$T_{1}A_{1}+T_{2}A_{2}=T_{1}B_{1}+T_{2}B_{2}$$$, the answer is infinity.

Else, WLOG, we let $$$T_{1}A_{1}+T_{2}A_{2} > T_{1}B_{1}+T_{2}B_{2}$$$. If $$$A_{1} > B_{1}$$$, then Takahashi and Aoki will never meet each other. The answer is 0. Now, we have solved all the corner cases. We shall move on to the general case. We shall call the period $$$T_{1}+T_{2}$$$. Now, we shall find the number of periods where Takahashi and Aoki meet each other. If we do some math, we get the number of periods to be $$$\lceil \frac{T_{1}(B_{1}-A_{1})}{(T_{1}A_{1}+T_{2}A_{2})-(T_{1}B_{1}+T_{2}B_{2})} \rceil$$$.

The number of times that Takahashi and Aoki meet each other is $$$2periods-1$$$ since every period they meet each other twice when Aoki overtakes Takahashi and Takahashi overtakes Aoki. We need to minus 1 since we do not count the first time they meet each other at the very start. We submit this and... WA.

Yes, we need to think about the case where $$$\frac{T_{1}(B_{1}-A_{1})}{(T_{1}A_{1}+T_{2}A_{2})-(T_{1}B_{1}+T_{2}B_{2})}$$$ is a whole number. In this case, we need to add one, as there will be one more time where Aoki will meet up with Takahashi but never overtake him. And now we get AC. (Thanks to AtillaAk for pointing out the mistake)


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By errorgorn, history, 14 months ago, In English

Example problem: (I dont have the link as this problem only exists in my school's private judge)

We have an array of n positive intergers and we need to group them into k segments such that each element of the array is in a segment and that all elements in a segment are contigous. The cost of a segment is the sum of the numbers in the segment squared.

We need to minimize the total cost of all the segments.

Example 1, we have n=5 and k=3. Where the array is [1,2,3,4,5]. The optimal answer is by grouping (1+2+3) , (4) and (5) into different segments. The total cost is 6^2 + 4^2 + 5^2 =77.

Example 2, we have n=4, k=2. Where the array is [1,1000000,3,4]. The optimal answer is by grouping (1,1000000) and (3,4) into different segments. The total cost is 1000001^2+7^2=10000200050

Now I asked some people and they said this can be solved by doing the lagrange (or aliens) speedup trick. We shall define a cost C. Then do a convex hull trick where we try to minimize the value of all our segments but we also add C to our cost whenever we make a new segment. Now, if C is INF, then we will only have 1 segment, while if C is 0 we will have n segments. So people claim that we can binary search on C to find some C where there will be K segments existing.

This allowed me to AC the problem, but my solution was not always correct as the test cases were weak. I thought of this test case: n=4, k=3. Where is array is [1,1,1,1] Now, when C is 2, we get 4 segments. But when C is 3, we get 2 segments only. Therefore when I ran my code against this case my code printed 8, even though the answer was (1+1)^2+1^2+1^2=6.

So I think I need someway to iterpolate the lagrange speedup trick. Anyone can help? My code is here.

For my code, the input format is:

n k

a1 a2 a3 a3... an

Where ai is the ith element in the array.

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