Publications
73 results found
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
Singular perturbation techniques allow the derivation of an aggregate model whose solution is asymptotically optimal for Markov decision processes with strong and weak interactions. We develop an algorithm that takes advantage of the asymptotic optimality of the aggregate model in order to compute the solution of the original model. We derive conditions for which the proposed algorithm has better worst case complexity than conventional contraction algorithms. Based on our complexity analysis, we show that the major benefit of aggregation is that the reduced order model is no longer ill conditioned. The reduction in the number of states (due to aggregation) is a secondary benefit. This is a surprising result since intuition would suggest that the reduced order model can be solved more efficiently because it has fewer states. However, we show that this is not necessarily the case. Our theoretical analysis and numerical experiments show that the proposed algorithm can compute the optimal solution with a reduction in computational complexity and without any penalty in accuracy.
Wright R, Stoianov I, Parpas P, et al., 2014, Adaptive Water Distribution Networks with Dynamically Reconfigurable Topology, Journal of Hydroinformatics
This paper presents a novel concept of adaptive water distribution networks withdynamically reconfigurable topology for optimal pressure control, leakage management and improved system resilience. The implementation of District Meter Areas (DMAs) has greatly assisted water utilities in reducing leakage. DMAs segregate water networks into small areas; the flow in and out of each area is monitored and thresholds are derived from the minimum night flow to trigger the leak localisation. A major drawback of the DMA approach is the reduced redundancy in network connectivity which has a severe impact on network resilience, incident management and water quality deterioration. The presented approach for adaptively reconfigurable networks integrates the benefits of DMAs for managing leakage with the advantages of large scale looped networks for increased redundancy in connectivity, reliability and resilience. Self-powered multi-function network controllers are designed and integrated with novel telemetry tools for high-speed time-time synchronised monitoring of the dynamic hydraulic conditions. A robust and computationally efficient optimization method based on sequential convex programming is developed and applied for the dynamic and adaptive reconfiguration of water distribution networks. An investigation is carried out using an operational network to evaluate the benefits of the proposed method for dynamically configurable water supply networks.
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: 9783527316847
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
Long-term planning for electric power systems, or capacity expansion, has traditionally been modeled using simplified models or heuristics to approximate the short-term dynamics. However, current trends such as increasing penetration of intermittent renewable generation and increased demand response requires a coupling of both the long and short term dynamics. We present an efficient method for coupling multiple temporal scales using the framework of singular perturbation theory for the control of Markov processes in continuous time. We show that the uncertainties that exist in many energy planning problems, in particular load demand uncertainty and uncertainties in generation availability, can be captured with a multiscale model. We then use a dimensionality reduction technique, which is valid if the scale separation present in the model is large enough, to derive a computationally tractable model. We show that both wind data and electricity demand data do exhibit sufficient scale separation. A numerical example using real data and a finite difference approximation of the Hamilton–Jacobi–Bellman equation is used to illustrate the proposed method. We compare the results of our approximate model with those of the exact model. We also show that the proposed approximation outperforms a commonly used heuristic used in capacity expansion models.
Parpas P, Wiesemann W, 2014, Editorial to computational techniques in management science, Computational Management Science, Vol: 11, Pages: 3-4, ISSN: 1619-697X
Tavares G, Parpas P, 2013, On the information-based complexity of stochastic programming, OPERATIONS RESEARCH LETTERS, Vol: 41, Pages: 622-626, ISSN: 0167-6377
Wright R, Stoianov I, Parpas P, 2013, Dynamic topology in water distribution networks, Computing and Control for the Water Industry (CCWI 2013), Publisher: Elsevier
A new approach for the operational management of water distribution networks is herein presented, which introduces district metered areas (DMA) with dynamic topology. The approach facilitates the operation of an open and adaptive network thatreverts back to the original DMA structure only at night for leakage detection purposes, therefore eliminating the disadvantages of a closed network structure such as reduced resilience to failure and suboptimal pressure management. The concept and technology have been implemented on a water distribution network in the UK, and a novel optimization method used for its control has been derived that is fast and reliable.
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
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
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
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- Citations: 16
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
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
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- Citations: 7
Kuhn D, Parpas P, Rustem B, 2011, Stochastic Optimization of Investment Planning Problems in the Electric Power Industry, Vol: 5, Pages: 215-230
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. © 2008 Wiley-VCH Verlag GmbH & Co. KGaA.
Ye K, Parpas P, Rustem B, 2011, Robust portfolio optimization: a conic programming approach, Computational Optimization and Applications, Pages: 1-19-1-19, ISSN: 0926-6003
Kleniati P-M, Parpas P, Rustem B, 2010, Decomposition-based method for sparse semidefinite relaxations of polynomial optimization problems, Journal of Optimization Theory and Applications, Vol: 145, Pages: 289-310
Kleniati P-M, Parpas P, Rustem B, 2010, Partitioning procedure for polynomial optimization, Journal of Global Optimization, Vol: 48, Pages: 549-567
Kleniati PM, Parpas P, Rustem B, 2009, Partitioning Procedure for Polynomial Optimization: Application to Portfolio Decisions with Higher Order Moments
We consider the problem of finding the minimum of a real-valued multivariate polynomialfunction constrained in a compact set defined by polynomial inequalities and equalities.This problem, called polynomial optimization problem (POP), is generally nonconvex andhas been of growing interest to many researchers in recent years. Our goal is to tackle POPsusing decomposition. Towards this goal we introduce a partitioning procedure. The problemmanipulations are in line with the pattern used in the Benders decomposition [1], namelyrelaxation preceded by projection. Stengle’s and Putinar’s Positivstellensatz are employed toderive the so-called feasibility and optimality constraints, respectively. We test the performanceof the proposed method on a collection of benchmark problems and we present thenumerical results. As an application, we consider the problem of selecting an investmentportfolio optimizing the mean, variance, skewness and kurtosis of the portfolio.
Parpas P, Rustem B, 2009, Algorithms for Minimax and Expected Value Optimization, Pages: 121-151
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- Citations: 1
Ye K, Parpas P, Rustem B, 2009, Bounding Option Prices Using SDP With Change Of Numeraire
Recently, given the first few moments, tight upper and lower boundsof the no arbitrage prices can be obtained by solving semidefinite programming(SDP) or linear programming (LP) problems. In this paper,we compare SDP and LP formulations of the European-style optionspricing problem and prefer SDP formulations due to the simplicity ofmoments constraints. We propose to employ the technique of changeof numeraire when using SDP to bound the European type of options.In fact, this problem can then be cast as a truncated Hausdorffmoment problem which has necessary and sufficient moment conditionsexpressed by positive semidefinite moment and localizing matrices.With four moments information we show stable numerical resultsfor bounding European call options and exchange options. Moreover,A hedging strategy is also identified by the dual formulation.
Parpas P, Rustem B, Wieland V, et al., 2009, Mean and variance optimization of non–linear systems and worst–case analysis, Computational Optimization and Applications, Vol: 43, Pages: 235-259
Kuhn D, Parpas P, Rustem B, et al., 2009, Dynamic mean-variance portfolio analysis under model risk, Journal of Computational Finance, Vol: 12, Pages: 91-115
Parpas P, Rustem B, 2009, Convergence analysis of a global optimization algorithm using stochastic differential equations, Journal of Global Optimization, Vol: 45, Pages: 95-110
Parpas P, Rustem B, 2009, An algorithm for the global optimization of a class of continuous minimax problems, Journal of optimization theory and applications, Vol: 141, Pages: 461-473
Parpas P, Rustem B, Pistikopoulos EN, 2009, Global optimization of robust chance constrained problems, Journal of Global Optimization, Vol: 43, Pages: 231-247
Parpas P, Rustem B, Wieland V, et al., 2009, Mean variance optimization of non-linear systems and worst-case analysis, Computational Optimization and Applications, Vol: 43, Pages: 235-259
Tsoukalas A, Parpas P, Rustem B, 2009, A smoothing algorithm for finite min-max-min problems, Optimization Letters, Vol: 3, Pages: 49-62
Maringer D, Parpas P, 2009, Global optimization of higher order moments in portfolio selection, Journal of Global Optimization, Vol: 43, Pages: 219-230
Kuhn D, Parpas P, Rustem B, 2008, Bound-based decision rules in multistage stochastic programming, Kybernetika, Vol: 44, Pages: 34-150
Rustem B, Zakovi, Parpas P, 2008, An interior point algorithm for continuous minimax: implementation and computation, Optimization Methods and Software, Vol: 23, Pages: 911-928-911-928
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