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Improved Assessment of the Flexibility Range of Distribution Grids Using Linear Optimization
Power Systems Computation Conference, 2018Increasing use of flexible resources in electrical grids is forcing grid operators to intensify their cooperation to maintain grid stability. The contribution of this paper is the improvement of a method that allows the representation of the aggregated ...
D. Contreras, K. Rudion
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Trajectory Optimization for High-Speed Trains via a Mixed Integer Linear Programming Approach
IEEE transactions on intelligent transportation systems (Print), 2022This paper proposes a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort. Besides, the proposed approach can also achieve energy-saving effects by optimizing the operation time between
Yuan Cao +3 more
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Journal of Algorithms, 1993
Summary: Let \(\Gamma_ 0\) be a set of \(n\) halfspaces in \(E^ d\) (where the dimension \(d\) is fixed) and let \(m\) be a parameter, \(n\leq m\leq n^{\lfloor d/2\rfloor}\). We show that \(\Gamma_ 0\) can be preprocessed in time and space \(0(m^{1+\delta}\)) (for any fixed \(\delta>0\)) so that given a vector \(c\in E^ d\) and another set \(\Gamma_ q\)
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Summary: Let \(\Gamma_ 0\) be a set of \(n\) halfspaces in \(E^ d\) (where the dimension \(d\) is fixed) and let \(m\) be a parameter, \(n\leq m\leq n^{\lfloor d/2\rfloor}\). We show that \(\Gamma_ 0\) can be preprocessed in time and space \(0(m^{1+\delta}\)) (for any fixed \(\delta>0\)) so that given a vector \(c\in E^ d\) and another set \(\Gamma_ q\)
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Algorithms for Linear-Quadratic Optimization
, 2021Linear-quadratic optimization problems Newton algorithms Schur and generalized Schur algorithms structure-preserving algorithms. Appendices: Comparison of Riccati solvers notation and abbreviations.
V. Sima
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Small-Data, Large-Scale Linear Optimization with Uncertain Objectives
Management Sciences, 2017Optimization applications often depend on a huge number of uncertain parameters. In many contexts, however, the amount of relevant data per parameter is small, and hence, we may only have imprecise estimates.
Vishal Gupta, Paat Rusmevichientong
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A Data-Driven Approach to Multistage Stochastic Linear Optimization
Management Sciences, 2023D. Bertsimas +2 more
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Order‐constrained linear optimization
British Journal of Mathematical and Statistical Psychology, 2017Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA ...
Joe W, Tidwell +3 more
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Abstract Optimal Linear Filtering
SIAM Journal on Control and Optimization, 2000The linear optimal filtering problems in infinite-dimensional Hilbert spaces and their extensions are investigated. The quality functional is allowed to be a general quadratic functional defined by a possibly degenerate operator. The solutions of the stable and the causal filtering problems are obtained.
Fomin, Vladimir N. +1 more
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Linear Multimodel Time Optimization
Optimal Control Applications and Methods, 2002AbstractA linear optimization problem with unknown parameters from a given finite set is tackled. The problem is to find therobust time‐optimal controltransferring a given initial point to a convex terminal compact setMforallunknown parameters in a shortest time. The robust maximum principle for this minimax problem is formulated.
Boltyanski, V., Poznyak, A.
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