TY - JOUR AU - Bujtás, Csilla AU - Rote, Günter AU - Tuza, Zsolt TI - Optimal strategies in fractional games: vertex cover and domination JF - ARS MATHEMATICA CONTEMPORANEA J2 - ARS MATH CONTEMPOR VL - 2024 PY - 2024 SN - 1855-3966 DO - 10.26493/1855-3974.2771.4df UR - https://m2.mtmt.hu/api/publication/34820138 ID - 34820138 LA - English DB - MTMT ER - TY - JOUR AU - Patkós, Balázs AU - Tuza, Zsolt AU - Vizer, Máté TI - Extremal Graph Theoretic Questions for q-Ary Vectors JF - GRAPHS AND COMBINATORICS J2 - GRAPH COMBINATOR VL - 40 PY - 2024 IS - 3 SN - 0911-0119 DO - 10.1007/s00373-024-02787-4 UR - https://m2.mtmt.hu/api/publication/34818984 ID - 34818984 AB - A q -graph H on n vertices is a set of vectors of length n with all entries from \{0,1,\dots ,q\} { 0 , 1 , ⋯ , q } and every vector (that we call a q -edge) having exactly two non-zero entries. The support of a q -edge {\textbf{x}} x is the pair S_{\textbf{x}} S x of indices of non-zero entries. We say that H is an s -copy of an ordinary graph F if |H|=|E(F)| | H | = | E ( F ) | , F is isomorphic to the graph with edge set \{S_{\textbf{x}}:{\textbf{x}}\in H\} { S x : x ∈ H } , and whenever v\in e,e'\in E(F) v ∈ e , e ′ ∈ E ( F ) , the entries with index corresponding to v in the q -edges corresponding to e and e' e ′ sum up to at least s . E.g., the q -edges (1, 3, 0, 0, 0), (0, 1, 0, 0, 3), and (3, 0, 0, 0, 1) form a 4-triangle. The Turán number \mathop {}\!\textrm{ex}(n,F,q,s) ex ( n , F , q , s ) is the maximum number of q -edges that a q -graph H on n vertices can have if it does not contain any s -copies of F . In the present paper, we determine the asymptotics of \mathop {}\!\textrm{ex}(n,F,q,q+1) ex ( n , F , q , q + 1 ) for many graphs F . LA - English DB - MTMT ER - TY - JOUR AU - Kegyes, Tamás AU - Kummer, Alex AU - Süle, Zoltán AU - Abonyi, János TI - Generally Applicable Q-Table Compression Method and Its Application for Constrained Stochastic Graph Traversal Optimization Problems JF - INFORMATION (BASEL) J2 - INFORMATION-BASEL VL - 15 PY - 2024 IS - 4 SP - 193 SN - 2078-2489 DO - 10.3390/info15040193 UR - https://m2.mtmt.hu/api/publication/34762966 ID - 34762966 AB - We analyzed a special class of graph traversal problems, where the distances are stochastic, and the agent is restricted to take a limited range in one go. We showed that both constrained shortest Hamiltonian pathfinding problems and disassembly line balancing problems belong to the class of constrained shortest pathfinding problems, which can be represented as mixed-integer optimization problems. Reinforcement learning (RL) methods have proven their efficiency in multiple complex problems. However, researchers concluded that the learning time increases radically by growing the state- and action spaces. In continuous cases, approximation techniques are used, but these methods have several limitations in mixed-integer searching spaces. We present the Q-table compression method as a multistep method with dimension reduction, state fusion, and space compression techniques that project a mixed-integer optimization problem into a discrete one. The RL agent is then trained using an extended Q-value-based method to deliver a human-interpretable model for optimal action selection. Our approach was tested in selected constrained stochastic graph traversal use cases, and comparative results are shown to the simple grid-based discretization method. LA - English DB - MTMT ER - TY - JOUR AU - Caro, Yair AU - Patkós, Balázs AU - Tuza, Zsolt TI - Connected Turán number of trees JF - ARS MATHEMATICA CONTEMPORANEA J2 - ARS MATH CONTEMPOR VL - Manuscript Published 2023-10-03 PY - 2024 SP - & SN - 1855-3966 DO - 10.26493/1855-3974.3109.e4b UR - https://m2.mtmt.hu/api/publication/34760346 ID - 34760346 AB - As a variant of the much studied Turán number, ex(n,F), the largest number of edges that an n-vertex F-free graph may contain, we introduce the connected Turán number exc(n,F), the largest number of edges that an n-vertex connected F-free graph may contain. We focus on the case where the forbidden graph is a tree. The celebrated conjecture of Erdős and Sós states that for any tree T, we have ex(n,T)≤(|T|−2)n2. We address the problem how much smaller exc(n,T) can be, what is the smallest possible ratio of exc(n,T) and (|T|−2)n2 as |T| grows. We also determine the exact value of exc(n,T) for small trees, in particular for all trees with at most six vertices. We introduce general constructions of connected T-free graphs based on graph parameters as longest path, matching number, branching number, etc. LA - English DB - MTMT ER - TY - JOUR AU - Kegyes, Tamás AU - Süle, Zoltán AU - Abonyi, János TI - Disassembly line optimization with reinforcement learning JF - CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH J2 - CEJOR VL - 2024 PY - 2024 SP - 1 SN - 1435-246X DO - 10.1007/s10100-024-00906-3 UR - https://m2.mtmt.hu/api/publication/34728824 ID - 34728824 AB - As the environmental aspects become increasingly important, the disassembly problems have become the researcher’s focus. Multiple criteria do not enable finding a general optimization method for the topic, but some heuristics and classical formulations provide effective solutions. By highlighting that disassembly problems are not the straight inverses of assembly problems and the conditions are not standard, disassembly optimization solutions require human control and supervision. Considering that Reinforcement learning (RL) methods can successfully solve complex optimization problems, we developed an RL-based solution for a fully formalized disassembly problem. There were known successful implementations of RL-based optimizers. But we integrated a novel heuristic to target a dynamically pre-filtered action space for the RL agent ( dl O pt RL algorithm) and hence significantly raise the efficiency of the learning path. Our algorithm belongs to the Heuristically Accelerated Reinforcement Learning (HARL) method class. We demonstrated its applicability in two use cases, but our approach can also be easily adapted for other problem types. Our article gives a detailed overview of disassembly problems and their formulation, the general RL framework and especially Q-learning techniques, and a perfect example of extending RL learning with a built-in heuristic. LA - English DB - MTMT ER - TY - JOUR AU - Kegyes, Tamás AU - Süle, Zoltán AU - Abonyi, János TI - Machine learning -based decision support framework for CBRN protection JF - HELIYON J2 - HELIYON VL - 10 PY - 2024 IS - 2 SN - 2405-8440 DO - 10.1016/j.heliyon.2024.e25946 UR - https://m2.mtmt.hu/api/publication/34570694 ID - 34570694 LA - English DB - MTMT ER - TY - JOUR AU - Éles, András AU - Heckl, István AU - Cabezas, Heriberto TI - Modeling of a Biomass-Based Energy Production Case Study Using Flexible Inputs with the P-Graph Framework JF - ENERGIES J2 - ENERGIES VL - 17 PY - 2024 IS - 3 SP - 687 SN - 1996-1073 DO - 10.3390/en17030687 UR - https://m2.mtmt.hu/api/publication/34555799 ID - 34555799 AB - In this work, a modeling technique utilizing the P-Graph framework was used for a case study involving biomass-based local energy production. In recent years, distributed energy systems gained attention. These systems aim to satisfy energy supply demands, support the local economy, decrease transportation needs and dependence on imports, and, in general, obtain a more sustainable energy production process. Designing such systems is a challenge, for which novel optimization approaches were developed to help decision making. Previous work used the P-Graph framework to optimize energy production in a small rural area, involving manure, intercrops, grass, and corn silage as inputs and fermenters. Biogas is produced in fermenters, and Combined Heat and Power (CHP) plants provide heat and electricity. A more recent result introduced the concept of operations with flexible inputs in the P-Graph framework. In this work, the concept of flexible inputs was applied to model fermenters in the original case study. A new implementation of the original decision problem was made both as a Mixed-Integer Linear Programming (MILP) model and as a purely P-Graph model by using the flexible input technique. Both approaches provided the same optimal solution, with a 31% larger profit than the fixed input model. LA - English DB - MTMT ER - TY - JOUR AU - Kalauz, Károly AU - Frits, Márton AU - Bertók, Botond TI - Algorithmic model generation for multi-site multi-period planning of clean processes by P-graphs JF - JOURNAL OF CLEANER PRODUCTION J2 - J CLEAN PROD VL - 434 PY - 2024 SN - 0959-6526 DO - 10.1016/j.jclepro.2023.140192 UR - https://m2.mtmt.hu/api/publication/34450611 ID - 34450611 LA - English DB - MTMT ER - TY - JOUR AU - Keszler, Anita AU - Tuza, Zsolt TI - Spectrum of 3-uniform 6- and 9-cycle systems over K (3) v − I JF - DISCRETE MATHEMATICS J2 - DISCRETE MATH VL - 347 PY - 2024 IS - 3 PG - 10 SN - 0012-365X DO - 10.1016/j.disc.2023.113782 UR - https://m2.mtmt.hu/api/publication/34397551 ID - 34397551 N1 - Machine Perception Research Laboratory, Institute for Computer Science and Control (SZTAKI), Kende u. 13–17, Budapest, 1111, Hungary Alfred Renyi Institute of Mathematics, Realtanoda u. 13–15, Budapest, 1053, Hungary Department of Computer Science and Systems Technology, University of Pannonia, Egyetem u. 10, Veszprem, 8200, Hungary Export Date: 7 December 2023 CODEN: DSMHA Correspondence Address: Tuza, Z.; Alfred Renyi Institute of Mathematics, Realtanoda u. 13–15, Hungary; email: tuza.zsolt@mik.uni-pannon.hu LA - English DB - MTMT ER - TY - JOUR AU - Miseta, Tamás AU - Fodor, Attila AU - Fogarassyné Vathy, Ágnes TI - Surpassing early stopping: A novel correlation-based stopping criterion for neural networks JF - NEUROCOMPUTING J2 - NEUROCOMPUTING VL - 567 PY - 2024 SP - 127028 SN - 0925-2312 DO - 10.1016/j.neucom.2023.127028 UR - https://m2.mtmt.hu/api/publication/34395727 ID - 34395727 LA - English DB - MTMT ER -