Guide to Competitive Programming

Laaksonen, Antti

doi:10.1007/978-3-030-39357-1

Cited by 7 publications

(8 citation statements)

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“…Similarly, the method that uses multi-view spectral clustering will use embeddings [E] 2 and [E] 3 . The method of clustering by voting is validated by determining the best results after co-training by employing classifiers and obtaining the best results after classification.…”

Section: Proposed Approachmentioning

confidence: 99%

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions

Stoica

Băbiceanu

Rebedea

et al. 2023

Preprint

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The problem of source code analysis using machine learning techniques has been gaining much attention recently as several powerful code embeddings methods have been created. Having available different embedding methods for source code opened the way for tackling many practical problems related to source code analysis. This paper addresses the issue of determining the number of distinct algorithmic strategies that may be found in a set of correct solutions for a competitive programming problem. We have investigated using five embedding methods with three algorithmic strategies in a data analysis pipeline that tackle the previously described issues on a newly created dataset consisting of 15 algorithmic problems. We propose an unsupervised algorithm that considers each embedding as a different dataset view. On each view, a hard clustering algorithm can be employed to split the correct solutions into K distinct algorithmic approaches, where K is a hyperparameter. In the ideal case, if all the algorithmic approaches in each view are determined correctly, a mapping could be done between each consecutive view such that each algorithmic approach from one view maps to a single corresponding algorithmic approach in the other. We are interested in determining the maximum subset of solutions that satisfy the assumption from above and use this subset as a base for a co-training procedure where an estimator employed on each view votes for the algorithmic approach of each solution which is not part of the subset. According to the results, the proposed unsupervised voting algorithm highly improves the baseline clustering approach. This improvement was observed across all problems in the dataset except one. Scale-up of the data analysis pipeline to datasets of thousands of problems may yield the ability to profoundly understand and learn about the innovative process of correctly designing and writing code in the context of competitive programming or even industry code.

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Section: Proposed Approachmentioning

confidence: 99%

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions

Stoica

Băbiceanu

Rebedea

et al. 2023

Preprint

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“…The first one is a segment tree with range updates. The good book for the data structure is [16]. The second one is heavy-light decomposition (heavy path decomposition) [19,11] for a tree.…”

Section: Toolsmentioning

confidence: 99%

“…It has O(n) time complexity for preprocessing and O k(log n) 2 for processing a query. It is based on data structures and techniques like a segment tree [16], heavy-light decomposition (heavy path decomposition) [19,11] for a tree and fast algorithms for computing Lowest Common Ancestor (LCA) [5,4].…”

Section: Introductionmentioning

confidence: 99%

A Fast Algorithm for Online k-servers Problem on Trees

Khadiev¹,

Yagafarov²

2020

Preprint

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We consider online algorithms for the k-servers problem on trees. There is an k-competitive algorithm for this problem, and it is the best competitive ratio. M. Chrobak and L. Larmore suggested it. At the same time, the existing implementation has O(n) time complexity, where n is a number of nodes in a tree. We suggest a new time-efficient implementation of the algorithm. It has O(n) time complexity for preprocessing and O k(log n) 2 for processing a query.

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“…Our algorithms uses several data structures and algorithmic ideas like segment tree [13], suffix array [15], rolling hash [9] and prefix sum [5]. Let us describe them in this section.…”

Section: Toolsmentioning

confidence: 99%

“…We suggest a classical algorithm with running time O (n + L + m(log n) 2 ) = Õ(n + L), where L = |s 1 | + • • • + |s m |, |s i | is a length of s i and Õ does not consider log factors. The algorithm uses segment tree [13] and suffix array [15] data structures, concepts of comparing string using rolling hash [9,7] and idea of prefix sum [5].…”

Section: Introductionmentioning

confidence: 99%

Classical and Quantum Algorithms for Constructing Text from Dictionary Problem

Khadiev¹,

Remidovskii²

2020

Preprint

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We study algorithms for solving the problem of constructing a text (long string) from a dictionary (sequence of small strings). The problem has an application in bioinformatics and has a connection with the Sequence assembly method for reconstructing a long DNA sequence from small fragments. The problem is constructing a string t of length n from strings s 1 , . . . , s m with possible intersections. We provide a classical algorithm with running time O n + L + m(log n) 2 = Õ(n + L) where L is the sum of lengths of s 1 , . . . , s m . We provide a quantum algorithm with running timeAdditionally, we show that the lower bound for the classical algorithm is Ω(n + L). Thus, our classical algorithm is optimal up to a log factor, and our quantum algorithm shows speed-up comparing to any classical algorithm in a case of non-constant length of strings in the dictionary.

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Guide to Competitive Programming

Cited by 7 publications

References 0 publications

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions

A Fast Algorithm for Online k-servers Problem on Trees

Classical and Quantum Algorithms for Constructing Text from Dictionary Problem

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