Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Dey, Santanu S."'
Gas networks are used to transport natural gas, which is an important resource for both residential and industrial customers throughout the world. The gas network design problem is a challenging nonlinear and non-convex optimization problem. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0cbea6249b9c77bdcadfbaed1c9a402
http://arxiv.org/abs/2307.07648
http://arxiv.org/abs/2307.07648
Recently, we proposed a class of inequalities called lifted bilinear cover inequalities, which are second-order cone representable convex inequalities, and are valid for a set described by a separable bilinear constraint together with bounds on varia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3b7bb920897a47a640d084e120a6a7a
In many operational applications, it is necessary to routinely find, within a very limited time window, provably good solutions to challenging mixed-integer linear programming (MILP) problems. An example is the Security-Constrained Unit Commitment (S
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dca5a1a6b8654e4ab984684f4367da31
Full strong-branching is a well-known variable selection rule that is known experimentally to produce significantly smaller branch-and-bound trees in comparison to all other known variable selection rules. In this paper, we attempt an analysis of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53eaf7838a40e8599a9d99ed222922cb
Branch-and-bound is the workhorse of all state-of-the-art mixed integer linear programming (MILP) solvers. These implementations of branch-and-bound typically use variable branching, that is, the child nodes are obtained by fixing some variable to an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8cff4baa73f417ce0633fbb724f41c7b
http://arxiv.org/abs/2007.15192
http://arxiv.org/abs/2007.15192
While transmission switching is known to reduce power generation costs, the difficulty of solving even DC optimal transmission switching (DCOTS) has prevented optimal transmission switching from becoming commonplace in real-time power systems operati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53af84ccb558013457e892bff5a10291
http://arxiv.org/abs/2003.10565
http://arxiv.org/abs/2003.10565
One of the most complicating factors in decentralized optimization for power systems is the modeling of power flow equations. Existing formulations for DC power flows either have limited scalability or are very dense and unstructured, making them uns
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3419d137786321b0049a7f34aa8c496
http://arxiv.org/abs/2001.07771
http://arxiv.org/abs/2001.07771
We study sets defined as the intersection of a rank-1 constraint with different choices of linear side constraints. We identify different conditions on the linear side constraints, under which the convex hull of the rank-1 set is polyhedral or second
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b84e72f1222ba73f4b60421c629dd4d9
http://arxiv.org/abs/1902.00739
http://arxiv.org/abs/1902.00739
Principal component analysis (PCA) is one of the most widely used dimensionality reduction tools in data analysis. The PCA direction is a linear combination of all features with nonzero loadings -- this impedes interpretability. Sparse PCA (SPCA) is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a032ccc6d4f90217529da53ffee1fcd
http://arxiv.org/abs/1810.09062
http://arxiv.org/abs/1810.09062
Principal component analysis (PCA) is one of the most widely used dimensionality reduction methods in scientific data analysis. In many applications, for additional interpretability, it is desirable for the factor loadings to be sparse, that is, we s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9f0f61f04a60ab1f8632388123afec2