Visible to the public Discrete and Continuous Optimization 2015

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Discrete and Continuous Optimization


Discrete and continuous optimization are mathematical approaches to problem solving. The research works cited here are primarily focused on continuous optimization. For Science of Security, they relate to cyber-physical systems, resilience, and composability. Some of the most important work is being done in control systems. They appeared in 2015.

Cao, Cen; Ni, Qingjian; Zhai, Yuqing, “A Novel Community Detection Method Based on Discrete Particle Swarm Optimization Algorithms in Complex Networks,” in Evolutionary Computation (CEC), 2015 IEEE Congress on, vol., no., pp. 171–178, 25–28 May 2015. doi:10.1109/CEC.2015.7256889
Abstract: The community structure is one of the most common and important attributes in complex networks. Community detection in complex networks has attracted much attention in recent years. As an effective evolutionary computation technique, particle swarm optimization (PSO) algorithm has become a candidate for many optimization applications. However, PSO algorithm was originally designed for continuous optimization. In this paper, an improved simple discrete particle swarm optimization (ISPSO) algorithm and a discrete particle swarm optimization with redefined operator (IDPSO-RO) algorithm are proposed in the discrete context of community detection problem. Furthermore, a community correcting strategy is used to optimize the results. The performance of the two algorithms is tested on three real networks with known community structures. The experiment results show that ISPSO and IDPSO-RO algorithms using community correcting strategy can detect community structures more efficiently without prior knowledge about the size of communities and the number of communities.
Keywords: Algorithm design and analysis; Complex networks; Image edge detection; Optimization; Particle swarm optimization; Sociology; Statistics (ID#: 15-7105)


Weiliang Zeng; Zheng, Y.R.; Schober, R., “Online Resource Allocation for Energy Harvesting Downlink MIMO Systems with Finite-Alphabet Inputs,” in Communications (ICC), 2015 IEEE International Conference on, vol., no., pp. 2142–2147, 8–12 June 2015. doi:10.1109/ICC.2015.7248642
Abstract: This paper proposes an online resource allocation algorithm for weighted sum rate maximization in energy harvesting downlink multiuser multiple-input multiple-output (MIMO) systems. Taking into account the discrete nature of the modulation and coding rates (MCRs) used in practice, we formulate a stochastic dynamic programming (SDP) problem to jointly design the MIMO precoders, select the MCRs, assign the subchannels, and optimize the energy consumption over multiple time slots with causal and statistical energy arrival information and statistical channel state information. Solving this high-dimensional SDP entails several difficulties: the SDP has a nonconcave objective function, the optimization variables are of mixed binary and continuous types, and the number of optimization variables is on the order of thousands. We propose a new method to solve this NP-hard SDP by decomposing the high-dimensional SDP into an equivalent three-layer optimization problem and show that efficient algorithms can be used to solve each layer separately. The decomposition reduces the computational burden and breaks the curse of dimensionality.
Keywords: MIMO communication; dynamic programming; energy consumption; energy harvesting; resource allocation; stochastic programming; MIMO precoders; energy consumption; energy harvesting downlink MIMO systems; finite-alphabet inputs; modulation and coding rates; multiple time slots; nonconcave objective function; online resource allocation; statistical channel state information; stochastic dynamic programming problem; three-layer optimization problem; weighted sum rate maximization; Downlink; Energy consumption; Joints; MIMO; Optimization; Transmitters; Wireless communication (ID#: 15-7106)


Fainekos, G., “Automotive Control Design Bug-Finding with the S-Taliro Tool,” in American Control Conference (ACC), 2015, vol., no., pp. 4096-4096, 1–3 July 2015. doi:10.1109/ACC.2015.7171969
Abstract: One of the important challenges in the Model Based Development (MBD) of automotive systems is the problem of verifying functional system properties. In its general form, the verification problem is undecidable due to the interplay between continuous and discrete system dynamics [1]. In this tutorial, we present the bounded-time temporal logic testing and verification problem for Cyber-Physical Systems (CPS) [2]. Temporal logics [3] can formally capture both state-space and real-time system requirements. For example, temporal logics can mathematically state requirements like “whenever the system switches to first gear, then it should not switch to second gear within 2.5 sec”. Our approach in tackling this challenging problem is to convert the verification problem into an optimization problem through a notion of robustness for temporal logics [4]. The robust interpretation of a temporal logic specification over a system trajectory quantifies “how much” the system trajectory satisfies or does not satisfy the specification. In general, the resulting optimization problem is non-convex and non-linear, the utility function is not known in closed-form and the search space is uncountable. Thus, stochastic search techniques are employed in order to solve the resulting optimization problem. We have implemented our testing and verification framework into a MATLAB (TM) toolbox called S-TaLiRo (System’s TemporAl LogIc Robustness) [5], [6]. In this tutorial, we will demonstrate how S-TaLiRo can provide answers to challenge problems from the automotive industry [7]-[10].
Keywords: automobile industry; automotive engineering; concave programming; control engineering computing; control system synthesis; embedded systems; formal specification; formal verification; nonlinear programming; search problems; state-space methods; stochastic programming; temporal logic; MBD; Matlab toolbox; S-TaLiRo tool; automotive control design bug finding; automotive industry; automotive systems; bounded-time temporal logic testing; continuous system dynamics; cyber-physical system; discrete system dynamics; functional system properties verification; model based development; nonconvex optimization problem; nonlinear optimization problem; real-time system requirements; state-space system requirements; stochastic search techniques; temporal logic specification; Automotive engineering; Cyber-physical systems; Mathematical model; Optimization; Real-time systems; Robustness; Tutorials (ID#: 15-7107)


Ying Cui; Medard, M.; Yeh, E.; Leith, D.; Duffy, K., “Optimization-Based Linear Network Coding for General Connections of Continuous Flows,” in Communications (ICC), 2015 IEEE International Conference on, vol., no., pp. 4492–4498, 8–12 June 2015. doi:10.1109/ICC.2015.7249030
Abstract: For general connections, the problem of finding network codes and optimizing resources for those codes is intrinsically difficult and little is known about its complexity. Most of the existing solutions rely on very restricted classes of network codes in terms of the number of flows allowed to be coded together, and are not entirely distributed. In this paper, we consider a new method for constructing linear network codes for general connections of continuous flows to minimize the total cost of edge use based on mixing. We first formulate the minimum-cost network coding design problem. To solve the optimization problem, we propose two equivalent alternative formulations with discrete mixing and continuous mixing, respectively, and develop distributed algorithms to solve them. Our approach allows fairly general coding across flows and guarantees no greater cost than any solution without inter-flow network coding.
Keywords: distributed algorithms; linear codes; minimisation; network coding; continuous mixing; cost minimization; discrete mixing; distributed algorithm; equivalent alternative formulation; optimization-based linear network coding; Complexity theory; Distributed algorithms; Linear codes; Minimization; Network coding; Optimization (ID#: 15-7108)


Ali, U.; Yuan Yan; Mostofi, Y.; Wardi, Y., “An Optimal Control Approach for Communication and Motion Co-Optimization in Realistic Fading Environments,” in American Control Conference (ACC), 2015, vol., no., pp. 2930–2935, 1–3 July 2015. doi:10.1109/ACC.2015.7171180
Abstract: We consider an energy co-optimization problem of minimizing the total communication and motion energy of a robot tasked with transmitting a given number of information bits while moving along a fixed path. The data is transmitted to a remote station over a fading channel, which changes along the trajectory of the robot. While a previous approach to the problem used a speed-based motion-energy model, this paper uses acceleration both as an input to the system and as a basis for the motion energy which is more realistic. Furthermore, while previous approaches posed the problem in discrete time, we formulate it in continuous time. This enables us to pose the problem in an optimal control framework amenable to the use of maximum principle. We then compute the optimal control input via an effective algorithm recently developed by us that converges very fast. We use practical models for channel fading and energy consumption: the channel quality is predicted based on actual measurements, and the energy models are based on physical principles. Simulation is used to solve a specific problem and demonstrate the efficacy of our proposed approach.
Keywords: fading channels; maximum principle; motion control; robots; trajectory control; energy co-optimization; fading channel; motion co-optimization; motion energy minimization; optimal control approach; optimal control framework; realistic fading environment; robot trajectory; total communication minimization; Acceleration; Fading; Optimal control; Probabilistic logic; Robot sensing systems; Trajectory (ID#: 15-7109)


Shareef, Ali; Shareef, Aliha; Yifeng Zhu, “Optrix: Energy Aware Cross Layer Routing Using Convex Optimization in Wireless Sensor Networks,” in Networking, Architecture and Storage (NAS), 2015 IEEE International Conference on, vol., no., pp. 141–150, 6–7 Aug. 2015. doi:10.1109/NAS.2015.7255235
Abstract: Energy minimization is of great importance in wireless sensor networks in extending the battery lifetime. One of the key activities of nodes in a WSN is communication and the routing of their data to a centralized base-station or sink. Routing using the shortest path to the sink is not the best solution since it will cause nodes along this path to fail prematurely. We propose a cross-layer energy efficient routing protocol Optrix that utilizes a convex formulation to maximize the lifetime of the network as a whole. We further propose, Optrix-BW, a novel convex formulation with bandwidth constraint that allows the channel conditions to be accounted for in routing. By considering this key channel parameter we demonstrate that Optrix-BW is capable of congestion control. Optrix is implemented in TinyOS, and we demonstrate that a relatively large topology of 40 nodes can converge to within 91 % of the optimal routing solution. We describe the pitfalls and issues related with utilizing a continuous form technique such as convex optimization with discrete packet based communication systems as found in WSNs. We propose a routing controller mechanism that allows for this transformation. We compare Optrix against the Collection Tree Protocol (CTP) and we found that Optrix performs better in terms of convergence to an optimal routing solution, for load balancing and network lifetime maximization than CTP.
Keywords: Algorithm design and analysis; Energy efficiency; Maintenance engineering; Optimized production technology; Routing; Routing protocols; Wireless sensor networks; Convex Optimization; Energy Aware Routing; Network Routing; Network Topology; Routing Algorithm; Simulation; TOSSIM; TinyOS; Wireless Sensor Networks (ID#: 15-7110)


Csiszar, A., “A Combinatorial Optimization Approach to the Denavit-Hartenberg Parameter Assignment,” in Advanced Intelligent Mechatronics (AIM), 2015 IEEE International Conference on, vol., no., pp. 1451–1456, 7–11 July 2015. doi:10.1109/AIM.2015.7222745
Abstract: Assigning a Denavit Hartenberg parameter table to a serial robot or a kinematic chain is a cumbersome task which confuses many novice roboticists. In this paper a combinatorial optimization approach to Denavit Hartenberg parameter assignment is proposed. The proposed combinatorial optimization approach eliminates the reliance on human experience when assigning Denavit-Hartenberg parameters. Using practical insights the parameter assignment problem is transferred from a continuous search space to a discrete search space hence enabling the use of combinatorial optimization techniques. The search space is reduced below a limit which makes the application of exhaustive search and branch and bound optimization techniques practicable. An algorithm is described which is capable of generating all practical Denavit Hartenberg parametrizations of a kinematic chain (including dummy frames) within an acceptable time frame. The obtained results are ranked based on a proposed criterion.
Keywords: combinatorial mathematics; optimisation; robot kinematics; tree searching; Denavit Hartenberg parameter table; Denavit Hartenberg parametrizations; Denavit-Hartenberg parameter assignment; branch and bound optimization techniques; combinatorial optimization; continuous search space; discrete search space; exhaustive search; kinematic chain; parameter assignment problem; roboticists; serial robot; DH-HEMTs; Joints; Kinematics; Optimization; Robot kinematics; Search problems (ID#: 15-7111)


Hongwei, Wang, “Adaptive Control Based on Multiple Sub-Models and Auxiliary Variables for Non-Uniformly Sampled Data Systems,” in Control Conference (CCC), 2015 34th Chinese, vol., no., pp. 2994–2999, 28–30 July 2015. doi:10.1109/ChiCC.2015.7260100
Abstract: For the control of non-uniformly sampled systems (NUSS), a new adaptive control method based on sub-models and auxiliary variables is proposed. First of all, the lifted state space model for a class of multi-rates systems non-uniformly sampled from their continuous-time systems are derived, and the corresponding discrete transfer function model is acquired by using mathematical theory derivation to analyze the state space model of NUSS. An auxiliary variables based identification algorithm is employed to confirm the discrete transfer function model by using the non-uniformly sampled data. The model of NUSS acquired from the identification algorithm is decomposed into the sub-models in accordance to optimization control principle. On this basis, the adaptive control method is obtained by designing the adaptive controller of each sub-model based on auxiliary variables. The parameter estimated-based adaptive control algorithm can virtually achieve optimal control and ensure that the closed-loop system is stable and globally convergent. Finally, the simulation example is studied to demonstrate the effectiveness of the proposed method.
Keywords: Adaptation models; Adaptive control; Algorithm design and analysis; Data models; Linear programming; Mathematical model; Noise; adaptive control; auxiliary variables; identification; multi-rates; non-uniformly sampling (ID#: 15-7112)


Tawfeeq, A.-B.L.; Eremia, M., “A New Technique of Reactive Power Optimization Random Selection Paths RSP,” in Advanced Topics in Electrical Engineering (ATEE), 2015 9th International Symposium on, vol., no., pp. 848–853, 7–9 May 2015. doi:10.1109/ATEE.2015.7133944
Abstract: This paper presents a new technique of Reactive Power Optimization called Random Selection Paths RSP, where a new implementation depending on limit the region search and choosing the candidates of the control variables randomly has been applied. The objective function of the proposed algorithm is to minimize the system active power loss. The control variables are generator bus voltages, transformer tap positions and switch-able shunt capacitor banks. The new technique can easily treat with the both of continuous and discrete control variables and has been applied for practical IEEE 6, IEEE 14 and IEEE 30 bus systems. The proposed algorithm shows better results as compared to previous work.
Keywords: continuous systems; discrete systems; optimisation; power capacitors; power system control; reactive power; transformers; IEEE 14 bus systems; IEEE 30 bus systems; IEEE 6 bus systems; RSP; continuous control variables; discrete control variables; generator bus voltages; random selection paths; reactive power optimization; switchable shunt capacitor banks; system active power loss; transformer tap positions; Generators; Genetic algorithms; Linear programming; Load flow; Optimization; Reactive power; Voltage control; Active power loss reduction; New optimization technique; Random Selection Path; Reactive Power Optimization (ID#: 15-7113)


Miyakawa, M.; Takadama, K.; Sato, H., “Directed Mating Using Inverted PBI Function for Constrained Multi-Objective Optimization,” in Evolutionary Computation (CEC), 2015 IEEE Congress on, vol., no., pp. 2929–2936, 25–28 May 2015. doi:10.1109/CEC.2015.7257253
Abstract: In evolutionary constrained multi-objective optimization, the directed mating utilizing useful infeasible solutions having better objective function values than feasible solutions significantly contributes to improving the search performance. This work tries to further improve the effectiveness of the directed mating by focusing on the search directions in the objective space. Since the conventional directed mating picks useful infeasible solutions based on Pareto dominance, all solutions are given the same search direction regardless of their locations in the objective space. To improve the diversity of the obtained solutions in evolutionary constrained multi-objective optimization, we propose a variant of the directed mating using the inverted PBI (IPBI) scalarizing function. The proposed IPBI-based directed mating gives unique search directions to all solutions depending on their locations in the objective space. Also, the proposed IPBI-based directed mating can control the strength of directionality for each solution’s search direction by the parameter θ. We use discrete m-objective k-knapsack problems and continuous mCDTLZ problems with 2-4 objectives and compare the search performances of TNSDM algorithm using the conventional directed mating and the proposed TNSDM-IPBI using IPBI-based directed mating. The experimental results shows that the proposed TNSDM-IPBI using the appropriate θ* achieves higher search performance than the conventional TNSDM in all test problems used in this work by improving the diversity of solutions in the objective space.
Keywords: Pareto optimisation; evolutionary computation; knapsack problems; search problems; IPBI-based directed mating; Pareto dominance; TNSDM algorithm; continuous mCDTLZ problems; discrete m-objective k-knapsack problems; evolutionary constrained multiobjective optimization; inverted PBI scalarizing function; objective function values; search directions; search performance; Search problems (ID#: 15-7114)


Gu Xinxin; Wen Jiwei; Peng Li, “Model Predictive Control for Continuous-Time Markov Jump Linear Systems,” in Control and Decision Conference (CCDC), 2015 27th Chinese, vol., no., pp. 2071–2074, 23–25 May 2015. doi:10.1109/CCDC.2015.7162262
Abstract: This paper mainly studies the continuous-time Markov Jump Linear Systems (MJLSs) problem based on model predictive control (MPC). Sufficient conditions of the optimization problem, which could guarantee the mean square stability of the close-loop MJLS, are given at every sample time. Since the MPC strategy is aggregated into continuous-time MJLSs, a discrete-time controller is employed to deal with a continuous-time plant and the adopted cost function not only refers to the knowledge of system state but also considers the sampling period. In addition, the feasibility of MPC scheme and the mean square stability of the MJLS are deeply discussed by using the invariant ellipsoid. Finally, the main results are verified by a numerical example.
Keywords: Markov processes; closed loop systems; continuous time systems; discrete time systems; linear systems; optimisation; predictive control; stability; stochastic systems; MPC strategy; close-loop MJLS; continuous-time MJLS; continuous-time Markov jump linear systems; continuous-time plant; cost function; discrete-time controller; invariant ellipsoid; mean square stability; model predictive control; optimization problem; sampling period; sufficient conditions; system state; Ellipsoids; Linear systems; Markov processes; Optimization; Predictive control; Robustness; Stability analysis; Continuous-time Markov jump linear systems; Invariant ellipsoid; Model predictive control (ID#: 15-7115)


Mini, V.; Sunil Kumar, T.K., “Diversity Enhanced Particle Swarm Optimization Based Optimal Reactive Power Dispatch,” in Advancements in Power and Energy (TAP Energy), 2015 International Conference on, vol., no., pp. 170–175, 24–26 June 2015. doi:10.1109/TAPENERGY.2015.7229612
Abstract: Reactive Power Dispatch (RPD) problem is a complex nonlinear problem involving integer, discrete and continuous types of control variables. This paper proposes a novel algorithm for solving the RPD problem using Diversity Enhanced Particle Swarm Optimization (DEPSO) technique. The proposed method offers an effective technique for solving Mixed Integer Discrete Continuous (MIDC) problems; hence suitable for the RPD problem. The effectiveness of the proposed method is reflected on the rounding off of control variables to the nearest integer or nearest available discrete values. With the implementation of the solution obtained in real time applications, the system becomes less prone to voltage instability. In this paper, DEPSO is applied to standard IEEE 30-bus test system. The results obtained are compared with those of basic PSO method.
Keywords: IEEE standards; integer programming; load dispatching; particle swarm optimisation; reactive power control; DEPSO technique; IEEE 30-bus test system; MIDC problem; RPD problem; diversity enhanced particle swarm optimization based optimal reactive power dispatch; mixed integer discrete continuous problem; Niobium; Planning; Diversity Enhanced PSO; Diversity factor; Mixed Integer Discrete Continuous problem; Reactive Power Dispatch (ID#: 15-7116)


Sogo, T.; Utsuno, T., “Simple Algebraic Structure of Taylor Expansion of Sampled-Data Systems,” in Control Conference (ASCC), 2015 10th Asian, vol., no., pp. 1–6, May 31 2015–June 3 2015. doi:10.1109/ASCC.2015.7244806
Abstract: The relation between the continuous-time model and the corresponding discrete-time model of sampled-data system has not been believed to be very simple. However, from the view point of Taylor expansion with respect to sample time, the relation is approximated by unexpectedly simple polynomials. In this paper, we show that there is a simple regularity in Taylor expansion for any sampled-data systems. Next, it is demonstrated that the regularity reduces symbolic calculation of the Taylor expansion. Finally, we apply the result to identification of continuous-time model from discrete-time input-output data of sampled-data systems based on optimization techniques.
Keywords: continuous time systems; discrete time systems; optimisation; polynomials; sampled data systems; Taylor expansion; algebraic structure; continuous-time model; discrete-time input-output data; discrete-time model; optimization technique; polynomial; sampled-data system; symbolic calculation; Control systems; Mathematical model; Polynomials; Sampled data systems; Taylor series; Transfer functions (ID#: 15-7117)


Kuang, Li; Wang, Feng; Li, Yuanxiang; Mao, Haiqiang; Lin, Min; Yu, Fei, “A Discrete Particle Swarm Optimization Box-Covering Algorithm for Fractal Dimension on Complex Networks,” in Evolutionary Computation (CEC), 2015 IEEE Congress on, vol., no., pp. 1396–1403, 25–28 May 2015. doi:10.1109/CEC.2015.7257051
Abstract: Researchers have widely investigated the fractal property of complex networks, in which the fractal dimension is normally evaluated by box-covering method. The crux of box-covering method is to find the solution with minimum number of boxes to tile the whole network. Here, we introduce a particle swarm optimization box-covering (PSOBC) algorithm based on discrete framework. Compared with our former algorithm, the new algorithm can map the search space from continuous to discrete one, and reduce the time complexity significantly. Moreover, because many real-world networks are weighted networks, we also extend our approach to weighted networks, which makes the algorithm more useful on practice. Experiment results on multiple benchmark networks compared with state-of-the-art algorithms show that this PSOBC algorithm is effective and promising on various network structures.
Keywords: Benchmark testing; Clustering algorithms; Complex networks; Fractals; Greedy algorithms; Optimization; Time complexity (ID#: 15-7118)


Dongjin Song; Wei Liu; Meyer, D.A.; Dacheng Tao; Rongrong Ji, “Rank Preserving Hashing for Rapid Image Search,” in Data Compression Conference (DCC), 2015, vol., no., pp. 353–362, 7–9 April 2015. doi:10.1109/DCC.2015.85
Abstract: In recent years, hashing techniques are becoming overwhelmingly popular for their high efficiency in handling large-scale computer vision applications. It has been shown that hashing techniques which leverage supervised information can significantly enhance performance, and thus greatly benefit visual search tasks. Typically, a modern hashing method uses a set of hash functions to compress data samples into compact binary codes. However, few methods have developed hash functions to optimize the precision at the top of a ranking list based upon Hamming distances. In this paper, we propose a novel supervised hashing approach, namely Rank Preserving Hashing (RPH), to explicitly optimize the precision of Hamming distance ranking towards preserving the supervised rank information. The core idea is to train disciplined hash functions in which the mistakes at the top of a Hamming-distance ranking list are penalized more than those at the bottom. To find such hash functions, we relax the original discrete optimization objective to a continuous surrogate, and then design an online learning algorithm to efficiently optimize the surrogate objective. Empirical studies based upon two benchmark image datasets demonstrate that the proposed hashing approach achieves superior image search accuracy over the state-of-the-art approaches.
Keywords: Hamming codes; binary codes; computer vision; cryptography; data compression; learning (artificial intelligence); optimisation; Hamming distance ranking; RPH technique; binary codes; data compression; discrete optimization; hash function; large-scale computer vision applications; online learning algorithm; rank preserving hashing technique; rapid image search; state-of-the-art approaches; supervised hashing approach; supervised rank information preserving; Accuracy; Algorithm design and analysis; Benchmark testing; Binary codes; Encoding; Hamming distance; Optimization; Hashing; Image Retrieval; Image Search (ID#: 15-7119)


Fresnedo, O.; Gonzalez-Coma, J.P.; Castedo, L., “Design of Analog Joint Source-Channel Coding Systems for Broadcast Channels with MSE Balancing,” in Signal Processing Advances in Wireless Communications (SPAWC), 2015 IEEE 16th International Workshop on, vol., no., pp. 595–599, June 28 2015–July 1 2015. doi:10.1109/SPAWC.2015.7227107
Abstract: We consider the transmission of discrete-time continuous amplitude source information symbols over Multiple-Input Multiple-Output (MIMO) Broadcast Channels (BCs) using analog Joint Source Channel Coding (JSCC). We propose a distributed scheme that consists of single-user analog mappings concatenated with a BC access scheme specifically designed for the BC. Two different access methods are considered depending on the level of channel knowledge at transmission: Code Division Multiple Access (CDMA) and linear Minimum Mean Square Error (MMSE). The resulting analog JSCC systems are optimized to satisfy the requirements corresponding to the user MSEs and the power constraint. The idea of MSE balancing is also introduced to guarantee the feasibility of the optimization problems. Computer simulations show that CDMA provides good performance, although better results are obtained when the linear MMSE codes are employed instead of the CDMA ones.
Keywords: MIMO communication; broadcast channels; channel coding; code division multiple access; least mean squares methods; source coding; CDMA; MIMO channel; MMSE; MSE balancing; analog joint source-channel coding systems; broadcast channels; code division multiple access; discrete time continuous amplitude source information symbols; linear minimum mean square error; multiple input multiple output channel; single user analog mappings; Channel coding; Distortion; Joints; Multiaccess communication; Transmitters (ID#: 15-7120)


Xuelai Sheng; Yu-Hong Wang, “The Research and Optimization of Hybrid System MLD Model Parameters Based on HYSDEL,” in Control and Decision Conference (CCDC), 2015 27th Chinese, vol., no., pp. 2395–2400, 23–25 May 2015. doi:10.1109/CCDC.2015.7162322
Abstract: The MLD model can be created by HYSDEL toolbox automatically. It provides great convenience for the MLD research. But the number of binary variables, auxiliary continuous variables and inequality constraints in the MLD model cannot satisfy our requirements. In this paper, we proposed three steps optimization to optimize the model parameters. It can effectively reduce the amount of binary variables, auxiliary continuous variables and inequality constraints in that MLD model established by HYSDEL. The proposed algorithm was illustrated in saturation characteristic function and promising results were achieved.
Keywords: continuous systems; discrete event systems; optimisation; specification languages; CVDS; DEDS; HYSDEL; MLD model parameter optimization; continuous variable dynamic system; discrete event dynamic system; hybrid system description language; Computational modeling; Linear matrix inequalities; Manuals; Mathematical model; Optimization; Predictive models; Standards; HYSDEL; MLD; Model Optimization; Saturation Characteristic (ID#: 15-7121)


Al-Khaleel, M.; Shu-Lin Wu, “Neumann-Neumann Waveform Relaxation Methods for Fractional RC Circuits,” in Information and Communication Systems (ICICS), 2015 6th International Conference on, vol., no., pp. 73–78, 7–9 April 2015. doi:10.1109/IACS.2015.7103205
Abstract: The Waveform Relaxation (WR) methods are recognized as efficient solvers for large scale circuits and attract a lot of attention in recent years due to their favorable advantages where they are ideally suited for the use of multiple parallel processors for problems with multiple time scales. However, applying classical WR techniques to strongly coupled systems leads to non-uniform convergence. Therefore, more uniform WR methods have been developed. This paper is concerned to generalize the Neumann-Neumann waveform relaxation (NN-WR) method invented recently for time-dependent PDEs to time-fractional circuits which seems to be a promising method in circuit simulations. By choosing the RC circuit in infinite size as the model, we perform a convergence analysis for the NN-WR method and this corresponds to the analysis of this method for PDEs at the semi-discrete level. The NN-WR method contains a free parameter, namely β, which has a significant effect on the convergence rate. For PDEs, the analysis at the space-time continuous level shows β = 1 over 4, while the analysis in this paper shows that, at the semi-discrete level, i.e., for the circuit problem, we can have a better choice which leads to much faster convergence in practical computing. A comparison with the so-called Robin WR is also included.
Keywords: RC circuits; large scale integration; partial differential equations; waveform analysis; Neumann-Neumann waveform relaxation; fractional RC circuits; large scale circuits; space-time continuous level; time-dependent PDEs; Capacitors; Communication systems; Convergence; Integrated circuit modeling; Mathematical model; Space heating; Fractional RC circuits; Parallel computing; Parameter optimization; Waveform relaxation (WR) (ID#: 15-7122)


Bin, Liu; Feng, Liu; Shengwei, Mei, “AC-Constrained Optimal Power Flow Considering Wind Power Generation Uncertainty in Radial Power Networks,” in Control Conference (CCC), 2015 34th Chinese, vol., no., pp. 2797–2801, 28–30 July 2015. doi:10.1109/ChiCC.2015.7260066
Abstract: Wind power generation uncertainty has imposed great challenge on power systems’ operation and should be considered when making an operation strategy. In this paper, we focus on optimal power flow (OPF) problem in radial power networks while considering wind power generation uncertainty. To cope with this uncertainty, the stochastic AC-constrained OPF (ACOPF) problem considering both continuous and discrete devices is formulated based on our previous work. The second order cone relaxation is employed to relax the original mixed integer nonconvex nonlinear programming (MINNLP) problem to a mixed integer second order cone programming (MISOCP) problem which can be solved by commercial solvers with high computation efficiency. The modified IEEE 33 bus distribution system is studied in this paper which validate the effectiveness of the proposed model.
Keywords: Computational modeling; Load flow; Optimization; Reactive power; Uncertainty; Wind power generation; optimal power flow; radial power networks; stochastic optimization; uncertainty set; wind power (ID#: 15-7123)


Leandro, M.A.C.; Colombo Junior, J.R.; Kienitz, K.H., “Robust D-Stability via Discrete Controllers for Continuous Time Uncertain Systems Using LMIs with a Scalar Parameter,” in Control and Automation (MED), 2015 23th Mediterranean Conference on, vol., no., pp. 644–649, 16–19 June 2015. doi:10.1109/MED.2015.7158819
Abstract: This paper addresses an alternative for the synthesis of a discrete-time stabilizing controller, taking into account requirements of D-stability via Linear Matrix Inequalities (LMIs) with a certain scalar parameter. Considering continuous time systems with polytopic uncertainty, this paper contributes with an alternative to incorporate D-stability requirements in the ∋2 and ∋ discrete-time controller synthesis from continuous-time D-stable regions via Euler’s approximation. From these design requirements, robust controllers were designed and implemented for a case study system of two cars connected through a spring.
Keywords: approximation theory; continuous time systems; control system synthesis; discrete time systems; linear matrix inequalities; robust control; uncertain systems; Euler approximation; LMIs; cars; continuous time uncertain systems; continuous-time D-stable regions; discrete controllers; discrete-time stabilizing controller synthesis; linear matrix inequalities; polytopic uncertainty; robust D-stability; robust controller design; scalar parameter; Approximation methods; Continuous time systems; Optimization; Robustness; Springs; Stability analysis; Uncertainty; D-stability; Linear Matrix Inequalities; Linear Systems; Robust Control (ID#: 15-7124)


Bin Liu; Feng Liu; Shengwei Mei; Yue Chen, “AC-Constrained Economic Dispatch in Radial Power Networks Considering Both Continuous and Discrete Controllable Devices,” in Control and Decision Conference (CCDC), 2015 27th Chinese, vol., no., pp. 6249–6254, 23–25 May 2015. doi:10.1109/CCDC.2015.7161937
Abstract: Economic dispatch (ED) is widely studied in power system optimization and is a typical application of optimal power flow (OFF). As more distributed generation resources integrated, e.g. micro-turbines and renewable generators, the AC-constrained ED (ACED) of distribution power networks (a typical radial power networks) is more controllable and the operation optimization of which is essentially a complicated Mixed Integer Nonconvex Nonlinear Programming (MLNNLP) problem with discrete controllable devices, e.g. transformers and compensating capacitors. In this paper, we studied ACED problem of radial power networks based on Branch Flow model. To make this problem tractable, the piecewise linear (PWE) and latest second-order cone (SOC) relaxation techniques are employed to relaxe ACED to a Mixed Integer Second-order Cone Programming (MISOCP) problem which can be efficiently solved by commercial solvers. Besides, we also discussed the exactness of SOC relaxation for ACED problem of radial power networks based on recent research achievements in this area. The modified IEEE 33 bus system is studied which validated the effectiveness and high computation efficiency of the proposed method.
Keywords: integer programming; load dispatching; nonlinear programming; power system economics; AC constrained economic dispatch; branch flow model; continuous controllable device; discrete controllable device; distributed power generation; distribution power network; latest second-order cone relaxation technique; mixed integer second-order cone programming; optimal power flow; piecewise linear technique; power system optimization; typical radial power networks; Economics; OPF; economic dispatch; piecewise linear; radial power networks; second-order cone relaxation (ID#: 15-7125)


Shuqin Li; Ziyu Shao; Jianwei Huang, “ARM: Anonymous Rating Mechanism for Discrete Power Control,” in Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2015 13th International Symposium on, vol., no., pp. 199-206,
25–29 May 2015. doi:10.1109/WIOPT.2015.7151073
Abstract: Wireless interference management through continuous power control has been extensively studied in the literature. However, practical systems often adopt discrete power control with a limited number of power levels and MCSs (Modulation Coding Schemes). In general, discrete power control is NP-hard due to its combinatorial nature. To tackle this challenge, we propose an innovative approach of interference management: ARM (Anonymous Rating Mechanism). Inspired by the successes of the simple Anonymous Rating Mechanism in Internet and E-commerce, we develop ARM as distributed near-optimal algorithm for solving the discrete power control problem (i.e., the joint scheduling, power allocation, and modulation coding adaption problem) under the physical interference model. We show that ARM achieves a close-to-optimal network throughput with a very low control overhead. We also characterize the performance gap of ARM due to the loss of rating information, and study the trade-off between such gap and the convergence time of ARM. Through comprehensive simulations under various network scenarios, we find that the optimality gap of ARM is small and such a small gap can be achievable with only a small number of power levels. Furthermore, the performance degradation is marginal if only limited local network information is available.
Keywords: computational complexity; discrete systems; power control; radio networks; radiofrequency interference; telecommunication control; ARM; NP-hard problem; anonymous rating mechanism; continuous power control; discrete power control; distributed near-optimal algorithm; modulation coding scheme; physical interference model; wireless interference management; Algorithm design and analysis; Interference; Level set; Markov processes; Optimization; Power control; Signal to noise ratio (ID#: 15-7126)


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