1,174 research outputs found

    Two-stage stochastic minimum s − t cut problems: Formulations, complexity and decomposition algorithms

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    We introduce the two‐stage stochastic minimum s − t cut problem. Based on a classical linear 0‐1 programming model for the deterministic minimum s − t cut problem, we provide a mathematical programming formulation for the proposed stochastic extension. We show that its constraint matrix loses the total unimodularity property, however, preserves it if the considered graph is a tree. This fact turns out to be not surprising as we prove that the considered problem is NP-hard in general, but admits a linear time solution algorithm when the graph is a tree. We exploit the special structure of the problem and propose a tailored Benders decomposition algorithm. We evaluate the computational efficiency of this algorithm by solving the Benders dual subproblems as max-flow problems. For many tested instances, we outperform a standard Benders decomposition by two orders of magnitude with the Benders decomposition exploiting the max-flow structure of the subproblems

    Trade policies in Central Asia after EAEU enlargement and after Russian WTO accession: regionalism and integration into the world economy revisited

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    This dataset reproduces empirical results for the paper: Andrzej Cieślik & Oleg Gurshev (2023) Trade policies in Central Asia after EAEU enlargement and after Russian WTO accession: regionalism and integration into the world economy revisited, Eurasian Geography and Economics, DOI: 10.1080/15387216.2022.2162098 It includes data, graphs, and 3SLS gravity analysis performed in the paper. This research was funded in whole by National Science Centre, Poland under PRELUDIUM 20 grant №2021/41/N/HS4/00759. For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission

    Covering of high-dimensional sets

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    Let (X,ρ)(\mathcal{X},\rho) be a metric space and λ\lambda be a Borel measure on this space defined on the σ\sigma-algebra generated by open subsets of X\mathcal{X}; this measure λ\lambda defines volumes of Borel subsets of X\mathcal{X}. The principal case is where X=Rd\mathcal{X} = \mathbb{R}^d, ρ\rho is the Euclidean metric, and λ\lambda is the Lebesgue measure. In this article, we are not going to pay much attention to the case of small dimensions dd as the problem of construction of good covering schemes for small dd can be attacked by the brute-force optimization algorithms. On the contrary, for medium or large dimensions (say, d10d\geq 10), there is little chance of getting anything sensible without understanding the main issues related to construction of efficient covering designs

    L’Autobiografia di Gesù Cristo di Oleg Zobern

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    In the present essay the author proposes a thematic and genre analysis of the new novel by the Russian writer Oleg Zobern, Autobiography of Jesus Christ, in the light of the most recent trends and cultural attitudes of contemporary Russian literature.Nel presente saggio l\u27autore propone un\u27analisi tematica e di genere del recentissimo romanzo dello scrittore russo Oleg Zobern, Autobiografia di Gesù Cristo, alla luce delle tendenze più recenti della letteratura russa contemporanea e dell\u27atteggiamento verso di essa del lettore di oggi

    Podcast With Oleg Benesch - Co-Author Of "Japan's Castles Citadels Of Modernity In War And Peace"

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    We caught up with Oleg Benesch to talk about his new book "Japan's Castles: Citadels of Modernity in War and Peace," co-authored with Ran Zwigenberg. In the podcast Dr Benesch talks about what motivated them to write a book about castles that is not military history; writing a book with another author who lives in another part of the world; the meaning of modernity; the ambiguous relationship between history and heritage; and why issues of “space” matter in history

    Arc routing problems

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    Random search for global optimization

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    A Sequential Quadratic Programming Algorithm for Equality-Constrained Optimization without Derivatives

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    In this paper, we present a new model-based trust-region derivative-free optimization algorithm which can handle nonlinear equality constraints by applying a sequential quadratic programming (SQP) approach. The SQP methodology is one of the best known and most efficient frameworks to solve equality-constrained optimization problems in gradient-based optimization. Our derivative-free optimization (DFO) algorithm constructs local polynomial interpolation-based models of the objective and constraint functions and computes steps by solving QP sub-problems inside a region using the standard trust-region methodology. As it is crucial for such model-based methods to maintain a good geometry of the set of interpolation points, our algorithm exploits a self-correcting property of the interpolation set geometry. To deal with the trust-region constraint which is intrinsic to the approach of self-correcting geometry, the method of Byrd and Omojokun is applied. Numerical experiments are carried out on a set of test problems from the CUTEr library and on a simulation-based engineering design problem
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