In this paper, we study the problem of optimizing a linear program whose variables are the answers to a conjunctive query. For this we propose the language LP(CQ) for specifying linear programs whose constraints and o...
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The linear programming model based on active power sensitivity ignores the influence of voltage and network loss, the preventive control strategy is difficult to meet the calculation accuracy requirements after AC pow...
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This paper presents a methodology for the creation of dynamic reservoir rule curves on the basis of the results of implicit stochastic optimization coupled with optimized demand hedging embedded as constraints to opti...
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This paper presents a methodology for the creation of dynamic reservoir rule curves on the basis of the results of implicit stochastic optimization coupled with optimized demand hedging embedded as constraints to optimization. The novelty of the method is a dynamic rule curve that always starts from the current storage level and projects a range of anticipated target levels in the immediate future based on the statistical analyses of the results of implicit stochastic optimization. The method is particularly useful in dry years when storage is not completely filled at the end of wet seasons. Such situations cannot be addressed with standard traditional rule curves, thus causing reservoir operators to base their decisions on mere judgment. The proposed method can be helpful in such situations. The method has been demonstrated on the Tawa reservoir in the Narmada River Basin in India.
The paper provides a novel Lower Bound (LB) Limit Analysis (LA) Finite Element (FE) model for the study at failure of masonry walls in two-way bending by means of full 3D elements. The method of hexahedral discretizat...
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The focus of this article is on ∞-gain analysis and the observer design, which are applicable to linear two-dimensional (2D) positive systems (PSs) featuring bounded time-varying delays. Firstly, a computation method...
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The current best-known performance guarantees for the extensively studied Traveling Salesman Problem (TSP) of determinate approximation algorithms is 3/2, achieved by Christofides' algorithm 47 years ago. This pap...
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The current best-known performance guarantees for the extensively studied Traveling Salesman Problem (TSP) of determinate approximation algorithms is 3/2, achieved by Christofides' algorithm 47 years ago. This paper investigates a new generalization problem of the TSP, termed the Minimum-Cost Bounded Degree Connected Subgraph (MBDCS) problem. In the MBDCS problem, the goal is to identify a minimum-cost connected subgraph containing n = |V| edges from an input graph G = (V, E) with degree upper bounds for particular vertices. We show that for certain special cases of MBDCS, the aim is equivalent to finding a minimum-cost Hamiltonian cycle for the input graph, same as the TSP. To appropriately solve MBDCS, we initially present an integer programming formulation for the problem. Subsequently, we propose an algorithm to approximate the optimal solution by applying the iterative rounding technique to solution of the integer programming relaxation. We demonstrate that the returned subgraph of our proposed algorithm is one of the best guarantees for the MBDCS problem in polynomial time, assuming P not equal N P. This study views the optimization of TSP as finding a minimum-cost connected subgraph containing n edges with degree upper bounds for certain vertices, and it may provide new insights into optimizing the TSP in future research.
Many problems in Internet of Things (IoT) can be cast as distributed optimization problems. For this reason, this paper considers a distributed online constrained optimization problem in IoT, where the local objective...
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Many problems in Internet of Things (IoT) can be cast as distributed optimization problems. For this reason, this paper considers a distributed online constrained optimization problem in IoT, where the local objective functions change with time. In order to solve this problem, distributed projected gradient descent methods are employed frequently. However, the computation of the projection operators is prohibitive in high-dimensional constrained optimization problem. To address the issue, we propose a distributed online learning algorithm based on the conditional gradient method over IoT systems, which avoids the costly projection steps. Moreover, when the local objective functions are strongly convex, we show that the regret bound of O(T) is achieved, where T is a time horizon. When the local objective functions are potentially non-convex, we also show that the algorithm converges to some stationary points at rate of O(T). In addition, we present simulation experiments to confirm the theoretical results. IEEE
Climate policy is transforming the energy system and the building sector. Since these sectors overlap, we need to understand how the short-term operational link between them impact their long-term development subject ...
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Climate policy is transforming the energy system and the building sector. Since these sectors overlap, we need to understand how the short-term operational link between them impact their long-term development subject to overlapping climate policies. This paper investigates how the integrated development of the European heat and electricity system is influenced by net-zero emission neighbourhoods that compensate own carbon emissions with local renewable energy. The study is made in the context of EUs emission trading system. In our approach, we soft-link two mathematical programming models to couple energy transition policies at the European level with the neighbourhood scale. Results suggest that zero emission neighbourhoods make European decarbonisation more cost-efficient. When low carbon energy technologies in neighbourhoods become competitive, investments in large-scale technologies are reduced on the European level, including nuclear and fossil gas power and heating. Thus, early policy support to neighbourhood technologies could prevent stranded assets later in the transition. Further, results imply that more stringent emission caps earlier could help avoiding CO2 allowance price spikes later.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
A large amount of research effort has been dedicated to adapting boosting for imbalanced classification. However, boosting methods are yet to be satisfactorily immune to class imbalance, especially for multi-class pro...
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A large amount of research effort has been dedicated to adapting boosting for imbalanced classification. However, boosting methods are yet to be satisfactorily immune to class imbalance, especially for multi-class problems. This is because most of the existing solutions for handling class imbalance rely on expensive cost set tuning for determining the proper level of compensation. We show that the assignment of weights to the component classifiers of a boosted ensemble can be thought of as a game of Tug of War between the classes in the margin space. We then demonstrate how this insight can be used to attain a good compromise between the rare and abundant classes without having to resort to cost set tuning, which has long been the norm for imbalanced classification. The solution is based on a lexicographic linear programming framework which requires two stages. Initially, class-specific component weight combinations are found so as to minimize a hinge loss individually for each of the classes. Subsequently, the final component weights are assigned so that the maximum deviation from the class-specific minimum loss values (obtained in the previous stage) is minimized. Hence, the proposal is not only restricted to two-class situations, but is also readily applicable to multi-class problems. Additionally, we also derive the dual formulation corresponding to the proposed framework. Experiments conducted on artificial and real-world imbalanced datasets as well as on challenging applications such as hyperspectral image classification and ImageNet classification establish the efficacy of the proposal.
We propose a distributed data-based predictive control scheme to stabilize a network system described by linear dynamics. Agents cooperate to predict the future system evolution without knowledge of the dynamics, rely...
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We propose a distributed data-based predictive control scheme to stabilize a network system described by linear dynamics. Agents cooperate to predict the future system evolution without knowledge of the dynamics, relying instead on learning a data-based representation from a single sample trajectory. We employ this representation to reformulate the finite-horizon linear Quadratic Regulator problem as a network optimization with separable objective functions and locally expressible constraints. We show that the controller resulting from approximately solving this problem using a distributed optimization algorithm in a receding horizon manner is stabilizing. We validate our results through numerical simulations.
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