58 results found
Parpas P, A multilevel proximal gradient algorithm for a class of composite optimization problems, SIAM Journal on Scientific Computing, ISSN: 1095-7197
Composite optimization models consist of the minimization of the sumof a smooth (not necessarily convex) function and a non-smooth convex function.Such models arise in many applications where, in addition to the composite natureof the objective function, a hierarchy of models is readily available. It is commonto take advantage of this hierarchy of models by first solving a low fidelity modeland then using the solution as a starting point to a high fidelity model. We adoptan optimization point of view and show how to take advantage of the availability ofa hierarchy of models in a consistent manner. We do not use the low fidelity modeljust for the computation of promising starting points but also for the computa-tion of search directions. We establish the convergence and convergence rate ofthe proposed algorithm. Our numerical experiments on large scale image restora-tion problems and the transition path problem suggest that, for certain classes ofproblems, the proposed algorithm is significantly faster than the state of the art.
Parpas P, Campos JS, A Multigrid Approach to SDP Relaxations of Sparse Polynomial Optimization Problems, SIAM Journal on Optimization, ISSN: 1052-6234
Parpas P, Rustem B, Towards A Grid Market
In this paper we discuss a basic framework for a grid computing market. It has long been argued that pricing of computer resources can act as a scheduling protocol. We take this idea to its natural conclusion by discussing the basic properties of such a model. We introduce agents that own computer resources on the grid. We allow the agents to trade resources as well as consume resources for the benefit of their own computing needs. The aim is to study the behavior of such agents and discuss existence of equilibria between the price process and consumption of resources. At such an equilibrium point all the resources are consumed as soon as they are made available, and the market is at zero net supply.
Howe S, Parpas P, 2017, Error bounds on the solution to an optimal control problem over clustered consensus networks, Pages: 25-32
Copyright © by SIAM. In this paper, we obtain a bound on the error term of the solution to a control constrained, linear-quadratic optimal control problem over a clustered consensus network. For large scale systems, the solution may be regarded as com- putationally infeasible due to the increase in dimensionality. However, the two time-scale property of the network that arises from the cluster formation indicates that the optimal control problem can be written in standard singularly per- turbed form. Thus, we are able to obtain a reduced dimen- sion optimal control problem over a reduced dimension net- work where individual nodes within a cluster are collapsed into an aggregate node. The solution to the reduced problem can be shown to be asymptotically equivalent in the singular perturbation parameter to the solution of the original prob- lem. However, for many values of this asymptotic result may fail to be of practical use. We improve on this result by applying a duality theory to the clustered network and derive an upper bound u and lower bound l on the solution to the optimal control problem that holds for arbitrary and, furthermore, satisfies the inequality j u-l-j = O as 0.
Malki K, Tosto MG, Mourino-Talin H, et al., 2017, Highly Polygenic Architecture of Antidepressant Treatment Response: Comparative Analysis of SSRI and NRI Treatment in an Animal Model of Depression, AMERICAN JOURNAL OF MEDICAL GENETICS PART B-NEUROPSYCHIATRIC GENETICS, Vol: 174, Pages: 235-250, ISSN: 1552-4841
Menke R, Abraham E, Parpas P, et al., 2017, Extending the Envelope of Demand Response Provision though Variable Speed Pumps, Pages: 584-591
© 2017 The Authors. Changes in power generation and supply and changes in water distribution systems are creating new opportunities for water utilities to enhance operational efficiency and income through the use of advanced control and optimisation. First, the increase in renewables penetration into the grid is causing a growth in energy storage schemes. Second, variable speed pumps are now fitted to most new systems and many existing water distribution systems are being retrofitted with variable speed pumps to improve the efficiency of the operation. We study how these trends can be jointly exploited to provide energy storage from a water distribution system. We investigate how variable speed drive pumps can enhance the ability of a water distribution network to provide demand response energy to the grid. We show that demand response provision from water distribution systems can be improved through the use of variable speed pumps. We also demonstrate that a network equipped with variable speed pumps can provide demand response profitably across a wider range of operating scenarios compared to a network equipped with only fixed-speed pumps. The results highlight another potential benefit of variable speed pumps in water distribution systems.
Baltean-Lugojan R, Parpas P, 2016, Robust Numerical Calibration for Implied Volatility Expansion Models, SIAM JOURNAL ON FINANCIAL MATHEMATICS, Vol: 7, Pages: 917-946, ISSN: 1945-497X
Hovhannisyan V, Parpas P, Zafeiriou S, 2016, MAGMA: Multilevel Accelerated Gradient Mirror Descent Algorithm for Large-Scale Convex Composite Minimization, SIAM JOURNAL ON IMAGING SCIENCES, Vol: 9, Pages: 1829-1857, ISSN: 1936-4954
Luong DVN, Parpas P, Rueckert D, et al., 2016, A Weighted Mirror Descent Algorithm for Nonsmooth Convex Optimization Problem, JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, Vol: 170, Pages: 900-915, ISSN: 0022-3239
Menke R, Abraham E, Parpas P, et al., 2016, Demonstrating demand response from water distribution system through pump scheduling, APPLIED ENERGY, Vol: 170, Pages: 377-387, ISSN: 0306-2619
Menke R, Abraham E, Parpas P, et al., 2016, Exploring Optimal Pump Scheduling in Water Distribution Networks with Branch and Bound Methods, WATER RESOURCES MANAGEMENT, Vol: 30, Pages: 5333-5349, ISSN: 0920-4741
Menke R, Abraham E, Parpas P, et al., 2015, Approximation of system components for pump scheduling optimisation, Computing and Control for the Water Industry (CCWI2015)- Sharing the Best Practice in Water Management, Publisher: ELSEVIER SCIENCE BV, Pages: 1059-1068, ISSN: 1877-7058
Parpas P, Ustun B, Webster M, et al., 2015, Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach, INFORMS JOURNAL ON COMPUTING, Vol: 27, Pages: 358-377, ISSN: 1091-9856
Wright R, Abraham E, Parpas P, et al., 2015, Control of water distribution networks with dynamic DMA topology using strictly feasible sequential convex programming, WATER RESOURCES RESEARCH, Vol: 51, Pages: 9925-9941, ISSN: 0043-1397
Wright R, Abraham E, Parpas P, et al., 2015, Optimized control of pressure reducing valves in water distribution networks with dynamic topology, Computing and Control for the Water Industry (CCWI2015)- Sharing the Best Practice in Water Management, Publisher: ELSEVIER SCIENCE BV, Pages: 1003-1011, ISSN: 1877-7058
Wright R, Herrera M, Parpas P, et al., 2015, Hydraulic resilience index for the critical link analysis of multi-feed water distribution networks, Computing and Control for the Water Industry (CCWI2015)- Sharing the Best Practice in Water Management, Publisher: ELSEVIER SCIENCE BV, Pages: 1249-1258, ISSN: 1877-7058
Wright R, Parpas P, Stoianov I, 2015, Experimental investigation of resilience and pressure management in water distribution networks, Computing and Control for the Water Industry (CCWI2015)- Sharing the Best Practice in Water Management, Publisher: ELSEVIER SCIENCE BV, Pages: 643-652, ISSN: 1877-7058
Ho CP, Parpas P, 2014, SINGULARLY PERTURBED MARKOV DECISION PROCESSES: A MULTIRESOLUTION ALGORITHM, SIAM JOURNAL ON CONTROL AND OPTIMIZATION, Vol: 52, Pages: 3854-3886, ISSN: 0363-0129
Kuhn D, Parpas P, Rustem B, 2014, Stochastic Optimization of Investment Planning Problems in the Electric Power Industry, Process Systems Engineering, Pages: 215-230, ISBN: 9783527631209
© 2014 Wiley-VCH Verlag GmbH & Co. KGaA. All rights reserved. Decisions on whether to invest in new power system infrastructure can have farreaching consequences. The timely expansion of generation and transmission capacities is crucial for the reliability of a power system and its ability to provide uninterrupted service under changing market conditions.We consider a local (e.g., regional or national) power system which is embedded into a deregulated electricity market. Assuming a probabilistic model for future electricity demand, fuel prices, equipment failures, and electricity spot prices, we formulate a capacity expansion problem which minimizes the sum of the costs for upgrading the local power system and the costs for operating the upgraded system over an extended planning horizon. The arising optimization problem represents a two-stage stochastic program with binary first-stage decisions. Solution of this problem relies on a specialized algorithm which constitutes a symbiosis of a regularized decomposition method and a branch-and-bound scheme.
Parpas P, Webster M, 2014, A stochastic multiscale model for electricity generation capacity expansion, EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, Vol: 232, Pages: 359-374, ISSN: 0377-2217
Parpas P, Wiesemann W, 2014, Editorial to computational techniques in management science, Computational Management Science, Vol: 11, Pages: 3-4, ISSN: 1619-697X
Wright R, Stoianov I, Parpas P, 2014, Dynamic topology in water distribution networks, 12th International Conference on Computing-and-Control-for-the-Water-Industry (CCWI), Publisher: ELSEVIER SCIENCE BV, Pages: 1735-1744, ISSN: 1877-7058
Wright R, Stoianov I, Parpas P, et al., 2014, Adaptive water distribution networks with dynamically reconfigurable topology, JOURNAL OF HYDROINFORMATICS, Vol: 16, Pages: 1280-1301, ISSN: 1464-7141
Casale G, Ragusa C, Parpas P, 2013, A Feasibility Study of Host-Level Contention Detection by Guest Virtual Machines, 5th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom), Publisher: IEEE, Pages: 152-157, ISSN: 2330-2194
Kong FW, Parpas P, Rustem B, 2013, Sum of Non-Concave Utilities Maximization for MIMO Interference Systems, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, Vol: 12, Pages: 1744-1751, ISSN: 1536-1276
Parpas P, Webster M, 2013, A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control, AUTOMATICA, Vol: 49, Pages: 1663-1671, ISSN: 0005-1098
Tavares G, Parpas P, 2013, On the information-based complexity of stochastic programming, OPERATIONS RESEARCH LETTERS, Vol: 41, Pages: 622-626, ISSN: 0167-6377
Luong DVN, Parpas P, Rueckert D, et al., 2012, Solving MRF Minimization by Mirror Descent, 8th International Symposium on Visual Computing (ISVC), Publisher: SPRINGER-VERLAG BERLIN, Pages: 587-598, ISSN: 0302-9743
Parpas P, Wiesemann W, 2012, Editorial, Computational Management Science, Vol: 9, Pages: 301-302, ISSN: 1619-697X
Webster M, Santen N, Parpas P, 2012, An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty, Computational Management Science, Vol: 9, Pages: 339-362, ISSN: 1619-697X
Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, two common simplifications are the use of two-stage models to approximate a multi-stage problem and exogenous formulations for inherently endogenous or decision-dependent uncertainties (in which the shock at time t+1 depends on the decision made at time t). In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change in a multi-stage setting. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. We show that increasing the variance of a symmetric mean-preserving uncertainty in abatement costs leads to higher optimal first-stage emission controls, but the effect is negligible when the uncertainty is exogenous. In contrast, the impact of decision-dependent cost uncertainty, a crude approximation of technology R & D, on optimal control is much larger, leading to higher control rates (lower emissions). Further, we demonstrate that the magnitude of this effect grows with the number of decision stages represented, suggesting that for decision-dependent phenomena, the conventional two-stage approximation will lead to an underestimate of the effect of uncertainty. © 2012 Springer-Verlag.
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