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News
July 9, 2026
HSE Economists Use Search Queries to Forecast Birth Rates
Researchers from the HSE Faculty of Economic Sciences have shown that the accuracy of birth rate forecasts for Russia can be improved by almost 50% by incorporating the dynamics of online search queries related to pregnancy and childbirth into forecasting models. In the best-performing models, the forecasting error fell from 4.6% to 3.2%. The findings have been published in Populations and Economics.
July 8, 2026
HSE Researchers Discover Who Eats Out in Russia-And Why
Around one-third of Russians (31.3%) rarely eat out or buy ready-made meals. The core group of active consumers—those who eat out or purchase prepared food almost every day or several times a week—accounts for only about 9% of the population. These are the findings of a study conducted by the HSE Institute for Social Policy. According to the researchers eating out is no longer a marker of high social status in Russia.
July 8, 2026
HSE University and RREDA Join Forces to Support 2026 Renewable Energy of the Planet Competition
HSE University and the Russia Renewable Energy Development Association (RREDA) have signed a partnership and information cooperation agreement to support Renewable Energy of the Planet—2026, a national competition with international participation for students and early-career researchers. Applications are open on the competition's website until September 20, 2026.

 

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Алгоритм ветвей и границ для задачи о формировании производственных ячеек

Программные продукты, системы и алгоритмы. 2017. № 4. С. 1–10.
Utkina I. E., Batsyn M. V.

The Cell Formation Problem (CFP) is an NP-hard optimization problem considered for cellular man- ufacturing systems. Because of its high computational complexity there have been developed a lot of heuristics and almost no exact algorithms for solving this problem. In this paper we suggest a branch- and-bound algorithm which provides exact solutions for the CFP with the grouping efficacy objective function. To linearize this fractional objective function we apply the Dinkelbach approach. Our algorithm finds optimal solutions for 24 of the 35 popular benchmark instances from literature and for the remaining instances it finds good solutions close to the best known. The difference in the grouping efficacy with the best known solutions is always less than 1.5%. 

Priority areas: IT and mathematics
Language: Russian
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Keywords: бикластеризацияBranch and Bound algorithmcell formation problemметод ветвей и границbiclusteringDinkelbach algorithmточное решение exact solutionФормирование производственных ячеекметод Динкельбаха
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