A. Nikolaev "On 1-Skeleton of the Cut Polytopes" in nomination "Best Theoretical Paper",
I. Davydov (joint work with A. Gabdullina, M. Shevtsova, D. Arkhipov) "Tabu Search for a Service Zone Clustering Problem" in nomination "Best Application Paper",
D. Yarmoshik (joint work with M. Persiianov) "On the Application of Saddle-Point Methods for Combined Equilibrium Transportation Models" in nomination "Best Young Authors Paper".
The Programme Committee congratulates all the laureates!
Awards is sponsored by BIA Technologies.
30.06.2024. Conference programme
The conference programme with parallel sessions schedule is published. Follow.
28.06.2024. Best Paper Award
Best paper awards competition is announced. Follow.
28.06.2024. The volume in LNCS
The first volume of MOTOR'24 proceedings is published in LNCS series.
A competition with six challenging applied problems is announced. Follow.
10.04.2024. Lists of articles for LNCS and CCIS
Dear Colleagues. Lists of accepted articles for the first volume (LNCS) and recommended articles for the second volume (CCIS) are published on the website.
15.03.2024. Change of deadlines for the notification of papers acceptance to proceedings volumes
Due to extension of submission deadline, the notification of papers acceptance to proceedings volumes is delayed till March, 31, 2024.
11.02.2024. Change of deadlines
The deadline for submission of papers and abstracts is extended to February, 29, 2024.
4.02.2024. Change of deadlines
The deadline for submission of papers and abstracts is extended to February, 19, 2024.
11.01.2024. Extension of deadlines for submitting abstracts
The deadline for abstracts submission is moved to February, 5, 2024. The link to the submission system will be provided here as soon as it will become available.
Prof. Xujin Chen
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China Undirected Networks Immune to the Informational Braess's Paradox
The Informational Braess's paradox exposes a counterintuitive phenomenon that disclosing additional road segments to some selfish travelers results in increased travel times for these individuals. This paradox expands upon the classic Braess's Paradox by relaxing the assumption that all travelers possess identical and complete information about the network. In this presentation, we explore structual conditions that prevent the informational Braess paradox in undirected networks.
Prof. Rentsen Enkhbat
Business school of National University of Mongolia, Ulaanbaatar, Mongolia Recent Advances in Game Theory
The title will be announced later.
Prof. Nikolai N. Guschinsky Belarus, Minsk Models and methods for optimization of electric public transport systems
The transition towards sustainable cities becomes an increasingly pressing issue because of the
growing awareness about the climate change. One of the critical transition directions is to reduce
the greenhouse gas emissions generated by transportation. One of efficient answers to this
challenge is the implementation of electric public transportation systems. In order to achieve the
best impact, it is imperative to design the infrastructure and the network of the public
transportation in an optimized way.
The following problems concerning strategic, tactical and operational aspects of the electric bus
planning process and scheduling are discussed: a) investment of electric bus fleet and charging
infrastructure; b) design of charging infrastructure; c) the electric vehicle scheduling; d) the
charging scheduling problem.
The models and optimization methods for different charging technologies are considered:
- slow plug-in chargers installed at bus depots;
- fast plug-in or pantograph chargers installed at terminals of bus lines or at bus stops;
- overhead contact lines or inductive (wireless) chargers that are used to recharge buses during
driving;
- battery swapping.
Prof. Roland Hildebrand Moscow Institute of Physics and Technology, Dolgoprudny, Russia Tuning methods for minimization of self-concordant functions with optimal control
In the last decade the usage of optimization methods for performance estimation and tuning of other optimization methods has became fashionable. For instance, semi-definite programming allows to analyze the behaviour of accelerated gradient descent methods at minimizing functions of different regularity. Detecting the worst-case performance with respect to the minimized function and maximizing this performance with respect to the parameters of the method allows to obtain the best parameter set.
Here we show how to perform a similar analysis of the Newton method when minimizing self-concordant functions. This task arises as a subproblem in more complex structured convex optimization problems such as semi-definite programming inside path-following methods. The appropriate framework for optimizing the parameters, in particular, the step length and the search direction, is optimal control theory. We present a general technique to use the Pontryagin maximum principle in the analysis of the Newton step on a self-concordant function and specialize it in several case studies.
The talk in part presents joint work with Anastasia Ivanova.
Prof. Elena V. Konstantinova China Three Gorges University, Yichang, China & Sobolev Institute of Mathematics, Novosibirsk, Russia Recent progress in domination theory
In this talk, some recent results in domination theory are
presented. In 2020, T.W. Haynes et al. introduced coalitions and
coalition partitions based on dominating sets in graphs as a
graph-theoretical model to describe political coalitions. The authors
have been studied the property of this concept and suggested a list of
open problems. In particular, it was suggested to study connected
coalitions based on connected dominating sets. In this talk we focus on
studying connected coalitions and their partitions in graphs with
emphasising to polynomial-time algorithms determining whether the
connected coalition number of a graph G of order n is either n or
n-1. The talk is based on joint works with S. Alikhani, D. Bakhshesh,
and H. Golmohammadi.
Prof. Panos M. Pardalos University of Florida, Gainesville, USA; HSE, Nizhny Novgorod, Russia AI and Optimization for a Sustainable Future
Advances in AI tools are progressing rapidly and demonstrating
the potential to transform our lives. The spectacular AI tools rely
in part on their sophisticated mathematical underpinnings (e.g.
optimization techniques and operations research tools), even
though this crucial aspect is often downplayed.
In this lecture, we will discuss progress from our perspective in
the field of AI and its applications in Energy systems and Sustainability.
Prof. Alexander A. Shananin, academician of RAS Moscow Institute of Physics and Technology, Moscow, Russia Mathematical modeling of the consumer loan market in Russia (joint talk with N.V. Trusov)
In this talk we present the mathematical description of the economic behavior of a rational household in consumer loan market. The modeling of the economic behavior of households is based on the concept of a rational representative economic agent and arises to F. Ramsey. The model is formalized as an optimal control problem on a finite time horizon. The household maximizes discounted consumption with constant risk aversion, managing the dynamics of its expenditures depending on the current parameters of the economic situation and the behavioral characteristics of the household itself. We consider an imperfect market when the interest rate on loans differs from the interest rate on deposits. The difference in interest rates on loans and deposits leads to non-smoothness of the right-hand side of the differential equation for the phase variable. This motivates to use the Pontryagin maximum principle in the form of F. Clark. Applying it, we obtain an area where the household does not interact with the banking system, the special regimes arise. If we tend the time horizon of an optimal control problem to infinity, it is possible to construct a synthesis. The synthesis allows us to determine an optimal control depending on the current value of the phase variable and the parameters of the economic situation. It depends on current interest rates and on the behavioral characteristics of a representative household. We develop and investigate a new model for the formation of interest rates on consumer loans based on an analysis of commercial interests and the logic of behavior of commercial banks. The model assumes that the borrowers’ incomes are described by a geometric Brownian motion. The commercial banks assess the default risk of borrowers. According to the Feynman–Kac formula, the assessment is reduced to solving a boundary value problem for partial differential equations. An analytical solution to this problem is constructed. It is possible to reduce the solution of the boundary value problem to the Cauchy problem for the heat equation with an external source and obtain a risk assessment in analytical form with a help of the Abel equation. The models of economic behavior of households in the consumer loan market and behavior of commercial banks are identified based on Russian statistics. A specialized software has been developed to analyze the demand for consumer credit. With its help, the problems of the consumer lending market in Russia are analyzed.
Prof. Zaiwen Wen
Peking University, China Exploring the Learning-based Optimization Algorithms
Abstract: The recent revolutionary progress of artificial intelligence has brought significant challenges and opportunities to mathematical optimization. In this talk, we briefly discuss two examples on the integration of data, models, and algorithms for the development of optimization algorithms: ODE-based learning to optimize and learning-based optimization paradigms for solving integer programming. We will also report a few interesting perspectives on formalization and automated theorem proving, highlighting their potential impact and relevance in contemporary mathematical optimization.
Bio: Wen Zaiwen, Professor at Peking University. He mainly studies optimization algorithms and theory and their applications in machine learning. He was awarded the China Youth Science and Technology Award in 2016 and Beijing Outstanding Youth Zhongguancun Award in 2020. He was funded by the National Ten Thousand Talents Program for Science and Technology Innovation. He is an associate editor of “Journal of Scientific Computing”, "Communications in Mathematics and Statistics", "Journal of the Operations Research Society of China", "Journal of Computational Mathematics" and a technical editor of "Mathematical Programming Computation".
Prof. Wenwu Yu Southeast University, China "Distributed Optimization +" in Networks: A New Framework
Distributed optimization is solved by the mutual collaboration among a group of
agents, which arises in various domains such as machine learning, resource allocation,
location in sensor networks and so on. In this talk, we introduce two kinds of
distributed optimization problems: (i) the agents share a common decision variable
and local constraints; (ii) the agents have their individual decision variables but that
are coupled by global constraints.
This talk comprehensively introduce the origin of distributed optimization, classical
works as well as recent advances. In addition, based on reinforcement learning,
shortest path planning, and mixed integer programming, we build the distributed
optimization framework of distributed optimization, and also discuss their
applications. Finally, we make a summary with future works for distributed
optimization.
Yury Kochetov Black box optimization for business applications
The black-box optimization models are characterized by lack of analytical
forms for the constraints and the objectives of the problem. In the
black-box methods, we need efficiently integrate known analytical part
with explicitly unknown correlations obtained from the business simulation
models. The direct use of classical global optimization methods is
prohibitive due to the lack of exact mathematical expressions. We cannot
calculate the derivatives or sub-gradients. Moreover, the computational
cost is high due to the simulations. Hence, we have to use the problem
specific methods to optimize such black-box systems efficiently. Important
applications stem from various disciplines: multi-echelon inventory
systems, chemical and mechanical engineering, financial management,
network topology design, and others. In this talk, we will discuss some
directions in this area and theoretical bounds for global optimization
methods. Successful cases for real-world applications will be presented.
Alexander Strekalovskiy New Principles of Non-Convex Optimization
The presentation addresses Elements of the Global Search Theory for DC
optimization problems for which the cost function and equality and
inequality constraints are given by DC functions, i.e. differences of convex
functions.
As well-know, for such problems, the modern tools of convex optimization
turn out to be incapable not only characterize and to find a global solution,
but even to escape a local pitfall.
In addition, sinceon a compact, and optimization problem with continuous
data can be approximated by some DC optimization problem. In particular,
an integer problem with boolean variables falls in DC, because is
equivalent to.
To begin with, we address the canonical DC problems such that convex
maximization and DC minimization over the standard feasible sets for
which the Global Optimality Conditions (GOCs) are presented.
In the focus of our investigation is the DC optimization problem from above
with DC equality and inequality constraints. With the help of the Exact
Penalty Theory of I.I Eremin and W. Zangwill, the DC problem is reduced to
a problem without constraints, the goal function of which is also DC. For the
latter DC problem, the new GOCs are proved which possess so-called
constructive (algorithmic) property. The property lays the foundation of
some Global Search Scheme using special Local Search Methods, in
"interior" of which one uses the modern and classical methods of Convex
Optimization.
In conclusion, we consider the applications of the developed approach
such that numerical search of Nash equilibrium, hierarchical optimization
problems, solutions of non-linear equation systems, etc.
Pavel Borisovsky Application of GPU computing to solving discrete optimization problems
Parallel computing on graphic processors (GPUs) is getting more and more popular. Since NVIDIA released the CUDA development tool, it has become convenient to use GPUs for general-purpose computing and not just for graphics display tasks. A feature of a GPU is the presence of a large (hundreds and thousands) number of cores, which allows to significantly speed up the calculation, but requires to design special parallel algorithms. While the development of traditional processors (CPUs) has recently slowed down, the characteristics of the GPU (number of cores, memory size, power consumption, and cost) are improving rapidly. In this tutorial, we will learn the basics of GPU computing in CUDA and OpenCL and briefly review the pros and cons of using GPUs in various discrete optimization algorithms.
Authors have the opportunity to submit their papers reporting on novel
results that are not published or submitted simultaneously to any journal or
another conference with refereed proceedings. Papers should be prepared in
the Springer LNCS Format, can have 12-15 pages, and submitted in PDF.
Pease, follow the
official Springer authors guidelines. Articles submitted to
journals or other peer-reviewed conferences will NOT BE accepted for
consideration.
It has been decided that two volumes of conference proceedings will be published in LNCS and CCIS series of Springer Nature as in the previous editions of MOTOR conference.
After uploading a paper, authors from Russian organizations, are requested to send a copy of conclusion of the expert commission on the possibility of open publication of the paper to the email of the organizing committee (motor24@ofim.oscsbras.ru).
This conference continues the long-term traditions of the Baikal, Yekaterinburg, Novosibirsk and Omsk international and all-Russian conferences, which have a rich history and are regularly held in the Urals, Siberia and the Far East, starting from the seventies of the 20th.
Conference name
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Baikal International Triennial School Seminar on Methods of Optimization and Their Applications
Since 2019, four well-known conferences have merged into one common cycle under the new name MOTOR, expanding geography and involving an increasing number of specialists in the field of optimization, operations research and their applications.
The conference will be held in the picturesque green area of Omsk at the Cronwel Park Nika. The Cronwell Park Nika Hotel meets high standards of hotel service quality, which allows it to be among the best hotels in Omsk. There are equipped conference rooms for events and excellent conditions for guests to relax.
The company BIA Technologies invites you to take part in the Best Paper Award.
Participants who have published their works or received a recommendation for publication in the Conference proceedings in the LNCS or CCIS series of Springer Nature are allowed to participate.
The award is held in three nominations: "Best Theoretical Paper Award", "Best Application Paper Award" and "Best Young Author Award".
Winners of the each category will get a money reward.
Announcement of winners: July 5, 2024.
Conference MOTOR-2024 location: Omsk.
You can find out more about the competition here (in Russian).
We are excited to announce the launch of a competition focused on solving industrial problems presented as challenges. This competition, provided in collaboration with one of our esteemed industrial partners, offers a unique opportunity for participants to showcase their problem-solving skills and innovative thinking. Whether you're a seasoned professional or an enthusiastic newcomer, this competition is your chance to make a significant impact in the industrial sector. This competition not only provides a platform for participants to demonstrate their technical skills but also fosters innovation and collaboration within the field. We encourage all interested individuals and teams to participate and contribute their creative solutions to real-world industrial challenges.
The Conference is supported by the Mathematical Center in Akademgorodok under the agreement No. 075-15-2022-282 with the Ministry of Science and Higher Education of the Russian Federation.