1,010 research outputs found
Two-stage stochastic minimum s − t cut problems: Formulations, complexity and decomposition algorithms
Full text linkWe 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
No full textThis 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
A Sequential Quadratic Programming Algorithm for Equality-Constrained Optimization without Derivatives
No full textIn 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
A Sequential Quadratic Programming Algorithm for Equality-Constrained Optimization without Derivatives
No full textIn 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
Robust Optimization
No full textAnalysts building mathematical models for real-world systems often face challenges with uncertain, noisy, incomplete, or erroneous data
Works of Oleg Pavlov in Context of Literary Reflection: A Review of Research
Full text linkThis article addresses the issue of literary reflection on the artistic legacy of contemporary Russian writer Oleg Pavlov. The study is based on critical and journalistic articles, literary analyses of the author’s works, and selected literary pieces. It presents an attempt at a systematic description, generalization, and analysis of Oleg Pavlov’s poetics. For the first time, the article proposes a periodization of the writer’s oeuvre. The author identifies and interprets the classical stages of Pavlov’s creative biography — early (“biographical”), mature (“critical-journalistic”), and late (“reflective”). It is established that literary criticism has offered a mixed assessment of the writer’s artistic mastery, presenting contradictory judgments ranging from condescending and tolerant to sharp and accusatory. On one hand, his prose is associated with the classical literary tradition; on the other hand, a cliché of the “gloomy” writer has emerged. The article highlights a sustained interest in Oleg Pavlov’s works within academic literary studies, identifying mythopoetic, philosophical, historical-cultural, and linguistic research directions. The author concludes that current philological reflection opens up research perspectives in the study of Pavlov’s poetics as a representative of “new realism” in contemporary Russian literature
Maximizing Matching Cuts
Full text linkGraph cut problems belong to a well-studied class of classical graph problems related to network connectivity, which is a central concept within theoretical computer science
Covering of high-dimensional sets
Full text linkLet be a metric space and be a Borel measure
on this space defined on the -algebra generated by open subsets of
; this measure defines volumes of Borel subsets of
. The principal case is where , is the Euclidean metric, and is the Lebesgue measure. In this
article, we are not going to pay much attention to the case of small dimensions
as the problem of construction of good covering schemes for small can
be attacked by the brute-force optimization algorithms. On the contrary, for
medium or large dimensions (say, ), there is little chance of getting
anything sensible without understanding the main issues related to construction
of efficient covering designs
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