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In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

This paper develops an equilibrium-based model for ex ante analysis of capacity withholding under financial virtual divestiture in a day-ahead market, including one dominant producer and a number of small producers considered as a price-taking competitive fringe. We model the offering problem of the dominant producer as a bilevel optimization problem. In the upper-level problem, the dominant producer derives optimal strategy of physical capacity withholding restricted by operational limits of its own generating units and conditions stated in the financial virtual divestiture. In the lower-level problem, market operator clears the day-ahead market on an hourly basis. We recast the bilevel optimization problem as a mathematical programming with equilibrium constraints problem and convert it to a mixed integer linear programming problem. We show how the application of the financial virtual divestiture may mitigate market power in the day-ahead market, through a case study. The numerical results demonstrate that the dominant producer can exploit technological diversification of its own generating units to exercise market power, even in the presence of this market power mitigation policy. [ABSTRACT FROM PUBLISHER]

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively. [ABSTRACT FROM AUTHOR]

An optimal planning model of the sectional switch and tie line for the distribution network is proposed considering the tradeoff between the system reliability and economy. First, the fault incidence matrices are established for the distribution network, and the explicit analytical expression of the system reliability index is realized by the algebraic operation of the fault incidence matrices and fault parameter vectors. Then, the 0–1 integer quadratically constrained optimization model is established for the sectional switch configuration considering the capacity constraint of the tie line. Finally, the sectional switches and tie lines are optimized jointly to ensure the economic investment and maximize the system reliability. The distribution network in Taiwan Power Company is used to verify the applicability of the proposed optimization method. Compared with the intelligent optimization algorithm, the mathematical programming method in this paper can always find the global optimal switch configuration quickly. In addition, the influence of the tie line capacity constraints on the sectional switch configuration is also considered, which can provide practical reference for the power network planners in the optimization design of the distribution network. [ABSTRACT FROM AUTHOR]

Energy-aware scheduling in a manufacturing environment with real-time energy pricing is a challenging problem. The purpose of this paper is to study a nonpreemptive scheduling problem on a single-machine to minimize the total tardiness and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multi-objective mathematical programming model. To achieve these objectives, we develop several new holistic genetic algorithms. The proposed model is solved via several methods including weighted sum method and multiobjective genetic algorithms based on dominance rank (GA-1), weighted sum aggregation (GA-2), dominance ranking procedure and crowding distance comparison (GA-3), and heuristic approach (GA-H). This paper illustrates that operations management, which is typically observed as a significant technique that allows manufacturing to operate in challenging data enabled environment in an industrial internet of things ecosystem, can also carefully optimize a production process to improve energy cost in order to manage environmental challenges. Thus, the findings provide information on when to start each job. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we illustrate how the results of the multiobjective model could be utilized in decision making using the technique for order preference by similarity to ideal solution method. [ABSTRACT FROM AUTHOR]

Recently, the concept of network virtualization and network slicing attracted significant attention from both industry and academia as a key component of the evolving 5G architecture to allow the efficient entrance of vertical industries and tackle increased aggregate traffic by flexible network re-configurability. However, the potential price to be paid for facilitating network slicing in a multi-tenant virtual network is the underutilization of the scarce wireless network resources due to the different tenant requirements and the inherent dynamics of the traffic. A potential way to avoid such sacrifice of radio resources is to allow efficient inter-tenant resource sharing. To this end, this paper proposes a novel optimization framework for flexible inter-tenant resource sharing embedded with transmission power control to aggressively improve network capacity, the utilization of wireless access resources, user data rate as well as energy efficiency. More specifically, we define two novel resource sharing mechanisms called tight coupling and loose coupling, respectively, via mixed integer linear programming formulations. Furthermore, two resource and power joint allocation algorithms are designed to solve the optimization problem in polynomial time. Based on 3GPP network parameterization, a rigorous analysis via a wide set of numerical investigations reveal that significant gains in network throughput, individual user rate, and energy efficiency can be achieved compared with current baseline network slicing methods and constant power resource sharing algorithms. [ABSTRACT FROM AUTHOR]

Graph cuts are widely used in computer vision. To speed up the optimization process and improve the scalability for large graphs, Strandmark and Kahl introduced a splitting method to split a graph into multiple subgraphs for parallel computation in both shared and distributed memory models. However, this parallel algorithm (the parallel BK-algorithm) does not have a polynomial bound on the number of iterations and is found to be non-convergent in some cases due to the possible multiple optimal solutions of its sub-problems. To remedy this non-convergence problem, in this paper, we first introduce a merging method capable of merging any number of those adjacent sub-graphs that can hardly reach agreement on their overlapping regions in the parallel BK-algorithm. Based on the pseudo-boolean representations of graph cuts, our merging method is shown to be effectively reused all the computed flows in these sub-graphs. Through both splitting and merging, we further propose a dynamic parallel and distributed graph cuts algorithm with guaranteed convergence to the globally optimal solutions within a predefined number of iterations. In essence, this paper provides a general framework to allow more sophisticated splitting and merging strategies to be employed to further boost performance. Our dynamic parallel algorithm is validated with extensive experimental results. [ABSTRACT FROM PUBLISHER]

To satisfy the rapidly growing demands of wireless communications, new structures have been proposed for the fifth-generation (5G) mobile communication networks, such as cloud radio access networks (C-RANs), which have advantages including high energy efficiency, large network capacity, and high flexibility. This paper concentrates on the problem of remote radio head (RRH) activation, user association, and power allocation in C-RANs. To tackle the problem with $l_{0}$ norm, we transform it into a mixed-integer nonlinear programming (MINLP) problem. Instead of solving it by centralized solvers, we propose a novel algorithm based on Benders decomposition, which can obtain the optimal solution of the MINLP problem. To solve the primal problem in Benders decomposition efficiently, we adopt the alternating direction method of multipliers (ADMM) to achieve a parallel implementation. To further reduce the complexity of solving the MINLP problem, a distributed two-stage iterative algorithm combining the ADMM and the max-sum algorithm is also proposed. The simulation results demonstrate that the first proposed algorithm can obtain the optimal solution, and the second proposed algorithm outperforms conventional algorithms significantly. [ABSTRACT FROM AUTHOR]

As there are various risks of failure in its execution, a composite web service (CWS) requires a transactional mechanism to guarantee its reliable execution. Though the existing service selection methods have considered that its transactional properties may affect its quality of service (QoS) such as its execution time, some of these methods can just give the locally optimal transactional CWS while others can give globally optimal CWS only under a given fixed transactional workflow. This paper addresses the issue of selecting and composing web services via a genetic algorithm (GA) and gives a transaction and QoS-aware selection approach. First, it introduces transactional properties of a single web service and CWS and the transactional rules used to compose them. Next, it conducts the performance analysis of basic workflow patterns such as sequential, parallel, selectable, and loop patterns and develops an algorithm to compute the execution time of a complex CWS. Then, it presents a GA-based approach, which takes into account the execution time, price, transactional property, stability, and penalty-factor, to achieve globally optimal service selection. Finally, this paper reports experimental results that compare the proposed approach with the exhaustive search algorithm, transactional-QoS-driven selection algorithm, and transactional service selection algorithm. The experimental results show that the proposed algorithm is efficient and effective and can give a globally optimal transactional CWS. [ABSTRACT FROM PUBLISHER]

Interconnection wide electric transmission system has been envisioned to facilitate the growth of renewable energy, enhance reliability, improve system operating efficiency, and reduce emissions. In this paper, we propose an explicit planning approach for transmission design at the national level. It has been applied to the U.S. system to design transmission overlays for multiple future generation scenarios. Associated simulation results suggest that a national transmission overlay provides economic, environmental, and system performance benefits. [ABSTRACT FROM PUBLISHER]

Large construction and infrastructure projects are a billion-dollar business, but few studies have addressed the integrated operations in this unique domain of the construction supply chain (CSC). The comparison between the CSC and a conventional supply chain enables us to examine its framework and establish a quantitative optimization model for the CSC. To introduce the integrated operations concept into the CSC, many uncertainties need to be first dealt with, for which a multiobjective uncertain optimization model is developed. As the optimization of the owner and fabricator's costs and the service level are the main objectives, a hybrid genetic algorithm with fuzzy-random method is developed to solve the optimization model. An integrated multiobjective purchasing and production planning model is then constructed and applied to a hydropower construction project in Southwest China. The results illustrate that efficient integrated operations are critical for the CSC performance. The optimization results also indicate that considering of uncertain rush orders and delay times can be vital for optimum CSC performance. With this proposed method, construction managers can quickly respond to changing uncertain demand. This paper has highlighted that project managers need to collaborate with other stakeholders to ensure optimal CSC performance. [ABSTRACT FROM AUTHOR]

In this paper, we study the multiattribute decision-making (MADM) problem with intuitionistic fuzzy values that represent information regarding alternatives on the attributes. Assuming that the weight information of the attributes is not known completely, we use an approach that utilizes the relative comparisons based on the advantage and disadvantage scores of the alternatives obtained on each attribute. The relative comparison of the intuitionistic fuzzy values in this research use all the three parameters, namely membership degree (“the more the better”), nonmembership degree (“the less the better”), and hesitancy degree (“the less the better”), thereby leading to the tradeoff values of all the three parameters. The score functions (advantage and disadvantage scores) used for this purpose are based on the positive contributions of these parameters, wherever applicable. Furthermore, these scores are used to obtain the strength and weakness scores leading to the satisfaction degrees of the alternatives. The optimal weights of the attributes are determined using a multiobjective optimization model that simultaneously maximizes the satisfaction degree of each alternative. The optimal solution is used for ranking and selecting the best alternative on the basis of the overall attribute values. To validate the proposed methodology, we present a numerical illustration of a real-world case. The methodology is further extended to treat MADM problem with interval-valued intuitionistic fuzzy information. Finally, a thorough comparison is done to demonstrate the advantages of the solution methodology over the existing methods used for the intuitionistic fuzzy MADM problems. [ABSTRACT FROM PUBLISHER]

Microgrids can be operated either grid-connected to reduce system losses and for peak shaving or islanded to increase reliability and provide backup power during utility outage. Such dual configuration capability imposes challenges on the design of the protection system. Fault current magnitudes will vary depending on the microgrid operating mode. In this paper, a microgrid protection scheme that relies on optimally sizing fault current limiters and optimally setting directional overcurrent relays is proposed. The protection scheme is optimally designed taking into account both modes of operation (grid-connected and islanded). The problem has been formulated as a constrained nonlinear programming problem and is solved using the genetic algorithm with the static penalty constraint-handling technique. The proposed approach is tested on two medium-voltage networks: a typical radial distribution system and on the IEEE 30-bus looped power distribution system equipped with directly connected conventional synchronous generators. [ABSTRACT FROM PUBLISHER]

This paper provides a methodology to assist a strategic producer in making informed decisions on generation investment. A single target year is considered with demand variations modeled through blocks. The strategic behavior of the producer is represented through a bilevel model: the upper-level considers both investment decisions and strategic production actions and the lower-level corresponds to market clearing. Prices are obtained as dual variables of power balance equations. Rival uncertainties (on offering and investment) are characterized through scenarios. The resulting model is a large-scale mixed-integer LP problem solvable using currently available branch-and-cut techniques. Results pertaining to an illustrative example and a case study are reported and discussed. [ABSTRACT FROM PUBLISHER]

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs. [ABSTRACT FROM AUTHOR]

In magnetic resonance imaging (MRI) magnets, field shimming technique is, in general, necessary to increase the field homogeneity in imaging region to an acceptable level after manufacturing. This paper presents a novel passive shimming method using permanently magnetized shimming pieces. In the method, the shimming design is modeled as an integer programming problem, and the distribution of shimming pieces is optimized. Through an application to a 0.3-T permanent magnet MRI system, the optimally designed shimming pieces are proven to improve the field homogeneity from 1456 to 784 ppm after just one iteration of shimming courses is carried out. [ABSTRACT FROM AUTHOR]

A professional service firm often faces challenges in matching its available workforce with the demands for service delivery. In this paper, we coin the labor strategy optimization (LSO) problem to prescribe an optimal staffing plan for meeting the target revenues of a professional service firm. It simultaneously optimizes the capacity, capability, location, and flexibility of a firm's workforce at the strategic level. Our modeling framework captures the richness and complexity of a general service transformation process. Mathematical programming based models are developed for both the deterministic and stochastic versions of the problem. A prototype of the LSO technology was implemented at the former HP Consulting & Integration organization. Through a comprehensive computational study, managerial insights are drawn and discussed about the optimal labor strategy under various supply-side risks and uncertainties. The optimal labor strategy provides a cost effective and reliable basis for more detailed workforce optimization concerning scheduling and assignment decisions. Our study also offers a data-driven approach to take advantage of large amount of disparate data, from different organizations and geographical locations of a firm, for improving the workforce planning decisions. [ABSTRACT FROM AUTHOR]

To reduce field-programmable gate array power, Vdd programmability has been recently proposed to select the Vdd level for interconnects and power-gate unused interconnects. However, Vdd-level converters used in the existing Vdd-programmable method consume a large amount of leakage. This paper proposes two ways to avoid using level converters in interconnects, namely; 1) tree-based level converter insertion (TLC) and 2) dual-Vdd tree-based level converter insertion (dTLC). TLC enforces that there is only one Vdd level within each routing tree, while dTLC can have different Vdd levels within a routing tree, but no VddL switch drives VddH switches. Dual-Vdd assignment algorithms were developed considering chip-level time slack allocation for maximum power reduction. The algorithms include TLC-S and dTLC-S, two power sensitivity-based algorithms with implicit time slack allocation, and dTLC-LP, a linear programming (LP)-based algorithm with explicit time slack allocation. All allocate time slack first to interconnects with higher power sensitivity and assign low Vdd to them for more power reduction. Experiments show that dTLC-LP obtains the lowest power consumption. Compared to dTLC-LP, dTLC-S obtains a slightly higher power consumption but runs three times faster. Compared to the existing segment-based level converter insertion for dual Vdd, dTLC-LP reduces interconnect power by 52.90% without performance loss for Microelectronics Center of North Carolina benchmark circuits. [ABSTRACT FROM AUTHOR]

Because of the strong competition in the equipment market, the aim of the manufacturers is to reduce the product price as much as possible, fulfill the high-quality standards, and improve the reliability and efficiency of the transformers. The capitalization of the costs enables to handle the different economic and technological parameters together in the same cost function. Large power autotransformers are usually designed for specific economic and technical conditions specified by the customer. At the tender design stage, the most economical solution has to be found in a limited time, and a great number of feasible transformer designs have to be compared to find the optimal solution. Nowadays, the solution of this design process is mainly based on the experience of the transformer designer, but mathematical programming models can facilitate the process. It is well known that the design of shell-type power transformers can be optimized by geometric programming. The constructed models of geometric programming can be solved accurately with the interior-point-method-based solvers. According to the results, this kind of modeling is not applicable for the core-form transformers. However, with branch-and-bound search, the geometric programming method can also be used for these cases. This paper introduces a new algorithm, which can also handle the autotransformer designs with different positions of the regulating winding by applying a more general metaheuristic routine to perform the calculation of the short-circuit impedance. [ABSTRACT FROM AUTHOR]

The performance of space shift keying (SSK) is known to be dominated by the minimum Euclidean distance (MED) in the received SSK constellation. In this paper, we propose a method of enhancing the MED in the received SSK constellation and improving both the attainable performance and the power efficiency by means of symbol scaling at the transmitter. To this aim, we formulate a pair of optimization problems, one for maximizing the MED subject to a specific transmit power constraint and one for minimizing the transmit power subject to a MED threshold. As these problems are NP-hard, we re-formulate their optimization using semidefinite relaxations, which results in convex problem formulations that can be efficiently solved using standard approaches. Moreover, we design pre-scaling techniques for imperfect channel state information at the transmitter, where the existing approaches are inapplicable. Our results show that the proposed schemes substantially improve the power efficiency of SSK systems with respect to state-of-the-art techniques by offering an improved performance for specific transmit power requirements or, equivalently, a transmit power reduction for a given MED threshold. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of joint optimal allocation and sizing of dynamic Var sources (ODynVarS) for short-term dynamic performance enhancement of power systems. A heuristic optimization based approach is proposed with the aim of achieving a desirable performance under multiple contingencies with respect to a reference short-term voltage response. Based on a pre-selected set of credible contingencies and potential candidate locations for device additions, the approach employs the mean-variance mapping optimization (MVMO) in combination with an integrated mixed-integer search strategy (IMIS) and two intervention schemes, to accelerate and guide the search process towards a techno-economically optimum solution. The first intervention is performed at fitness evaluation stage to screen economic solutions which entail technical feasibility and to reduce computational effort. IMIS and the second intervention is concerned with MVMO's knowledge management, which facilitates the search for solutions comprising a small number of devices with relatively large VAr capacity. The approach is tested using the IEEE 39-bus New England system. [ABSTRACT FROM AUTHOR]

Two-stage stochastic optimization, in which a long-term master problem is coupled with a family of short-term subproblems, plays a critical role in various application areas. However, most existing algorithms for two-stage stochastic optimization only work for special cases, and/or are based on the batch method, which requires huge memory and computational complexity. To the best of our knowledge, there still lack efficient and general two-stage online stochastic optimization algorithms. This paper proposes a two-stage online successive convex approximation (TOSCA) algorithm for general two-stage nonconvex stochastic optimization problems. At each iteration, the TOSCA algorithm first solves one short-term subproblem associated with the current realization of the system state. Then, it constructs a convex surrogate function for the objective of the long-term master problem. Finally, the long-term variables are updated by solving a convex approximation problem obtained by replacing the objective function in the long-term master problem with the convex surrogate function. We establish the almost sure convergence of the TOSCA algorithm and customize the algorithmic framework to solve three important application problems. Simulations show that the TOSCA algorithm can achieve superior performance over existing solutions. [ABSTRACT FROM AUTHOR]

Random hypothesis generation is central to robust geometric model fitting in computer vision. The predominant technique is to randomly sample minimal subsets of the data, and hypothesize the geometric models from the selected subsets. While taking minimal subsets increases the chance of successively “hitting” inliers in a sample, hypotheses fitted on minimal subsets may be severely biased due to the influence of measurement noise, even if the minimal subsets contain purely inliers. In this paper we propose Random Cluster Models, a technique used to simulate coupled spin systems, to conduct hypothesis generation using subsets larger than minimal. We show how large clusters of data from genuine instances of the model can be efficiently harvested to produce accurate hypotheses that are less affected by the vagaries of fitting on minimal subsets. A second aspect of the problem is the optimization of the set of structures that best fit the data. We show how our novel hypothesis sampler can be integrated seamlessly with graph cuts under a simple annealing framework to optimize the fitting efficiently. Unlike previous methods that conduct hypothesis sampling and fitting optimization in two disjoint stages, our algorithm performs the two subtasks alternatingly and in a mutually reinforcing manner. Experimental results show clear improvements in overall efficiency. [ABSTRACT FROM PUBLISHER]

An effective means for analyzing the impact of novel operating schemes on power systems is time-domain simulation, for example, for investigating optimization-based curtailment of renewables to alleviate voltage violations. Traditionally, interior-point methods are used for solving the non-convex AC optimal power flow (OPF) problems arising in this type of simulation. This paper presents an alternative algorithm that better suits the simulation framework, because it can more effectively be warm started, has linear computational and memory complexity in the problem size per iteration and globally converges to Karush–Kuhn–Tucker (KKT) points with a linear rate if they exist. The algorithm exploits a difference-of-convex-functions reformulation of the OPF problem, which can be performed effectively. Numerical results are presented comparing the method to state-of-the-art OPF solver implementations in MATPOWER, leading to significant speedups compared to the latter for smaller systems and comparable performance for larger cases. [ABSTRACT FROM PUBLISHER]

This paper studies the joint transceiver design and power splitting (JTDPS) for a downlink multiuser multiple-input multiple-output (MU-MIMO) simultaneous wireless information and power transfer (SWIPT) system. Aiming to minimize the transmit power by jointly optimizing the transmitter at a base station, the power splitting (PS) factors, and information decoding receivers at mobile stations (MSs) subject to both the predefined mean-square error (MSE) and the given energy harvesting (EH) constraints. The formulated problem is a nonconvex JTDPS optimization problem, which cannot be solved directly. To solve it, an iterative optimization framework composed of joint transmitter and PS factor optimization (JTxPS) subproblem and a receiver side minimum mean-square error (MMSE) minimization subproblem is proposed. By iteratively solve the two subproblems, the original optimization problem is solved. The sufficient and necessary conditions for the feasibility of the formulated JTDPS and the JTxPS problems are analyzed. Theoretical analysis shows that the JTxPS subproblem is also nonconvex and can be solved by using convex semidefinite programming (SDP). Considering the high computational complexity of the SDP-based scheme, a low-complexity suboptimal scheme based on the traditional MMSE transceiver design is also proposed. Simulations are done to validate the theoretical analysis, and results show the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

This paper deals with day-ahead static security assessment with respect to a postulated set of contingencies while taking into account uncertainties about the next day system conditions. We propose a heuristic approach to compute the worst-case under operation uncertainty for a contingency with respect to overloads. We formulate this problem as a non-convex nonlinear bilevel program that we solve approximately by a heuristic approach which relies on the solution of successive optimal power flow (OPF) and security-constrained optimal power flow (SCOPF) problems of a special type. The method aims at revealing those combinations of uncertainties and contingencies for which the best combination of preventive and corrective actions would not suffice to ensure security. Extensive numerical results on a small, a medium, and a very large system prove the interest of the approach. [ABSTRACT FROM PUBLISHER]

This paper deals with day-ahead power systems security planning under uncertainties by posing an optimization problem over a set of power injection scenarios that could show up the next day and modeling the next day's real-time control strategies aiming at ensuring security with respect to contingencies by a combination of preventive and corrective controls. We seek to determine whether and which day-ahead decisions must be taken so that, for scenarios over the next day, there still exists an acceptable combination of preventive and corrective controls ensuring system security for any postulated contingency. We formulate this task as a three-stage feasibility checking problem, where the first stage corresponds to day-ahead decisions, the second stage to preventive control actions, and the third stage to corrective post-contingency controls. We propose a solution approach based on the problem decomposition into successive optimal power flow (OPF) and security-constrained optimal power flow (SCOPF) problems of a special type. Our approach is illustrated on the Nordic32 system and on a 1203-bus model of a real-life system. [ABSTRACT FROM PUBLISHER]

Solution approaches to chance constrained programming (CCP) have been recently developed and applied in many areas for optimization under uncertainty. Due to the nonlinear model with multiple uncertain variables as well as multiple output constraints, CCP has not been directly applied to optimal power flow (OPF) under uncertainty. The objective of this paper is twofold. First, we introduce the CCP approach to OPF under uncertainty and analyze the computational complexity of the chance constrained OPF. Second, the effectiveness of implementing a back-mapping approach and a linear approximation of the nonlinear model equations to solve the formulated CCP problem is investigated. Load power uncertainties are considered as multivariate random variables with correlated normal distribution. Based on both the nonlinear and the linearized model, results of a five-bus system and the IEEE 30-bus test system are presented to demonstrate the scope of chance constrained OPF. [ABSTRACT FROM PUBLISHER]

This paper proposes an active-reactive power dispatch procedure that is expressed via a bilevel optimization problem whose upper and lower level criteria are, respectively, the minimum opportunity cost and minimum offered price of active power. It analyzes the impact of minimizing such criteria on system operation, and it investigates the behavior of marginal prices and opportunity costs and their use in mechanisms to compensate generators for power provision. The bilevel problem is solved using a version of interior point methods derived for mathematical programs with complementarity constraints. Results obtained with three test systems are analyzed. [ABSTRACT FROM PUBLISHER]

Through-silicon via (TSV)-based 3-D stacked ICs (SICs) are becoming increasingly important in the semiconductor industry. In this paper, we address test architecture optimization for 3-D stacked ICs implemented using TSVs. We consider two cases, namely 3-D SICs with die-level test architectures that are either fixed or still need to be designed. We next present mathematical programming techniques to derive optimal solutions for the architecture optimization problem for both cases. Experimental results for three handcrafted 3-D SICs comprising of various systems-on-a-chip (SoCs) from the ITC'02 SoC test benchmarks show that compared to the baseline method of sequentially testing all dies, the proposed solutions can achieve significant reduction in test length. This is achieved through optimal test schedules enabled by the test architecture. We also show that increasing the number of test pins typically provides a greater reduction in test length compared to an increase in the number of test TSVs. Furthermore, we show that shorter test lengths are generally achieved with the larger, more complex dies lower in the stack. This is because test data must pass through every die lower in a stack in order to reach its target die, and with the larger dies lower in the stack, more test bandwidth may be provided to these dies using fewer routing resources. [ABSTRACT FROM PUBLISHER]

Deregulation of the electric power industry has introduced new uncertainties for market participants and made planning of transmission expansion more difficult. More flexible transmission expansion plans are needed, to cope with the increased risks. In this paper, a novel planning approach is proposed to meet the above challenge. In our approach, the planning process is modeled as a mixed integer nonlinear programming (MINLP) problem, so that conflicting objectives can be optimized simultaneously. To minimize planning risks, our method identifies several scenarios based on statistics and expert knowledge; the most flexible expansion plan is selected as the plan that has least adaptation cost. The proposed method is tested with the IEEE 14-bus system. Promising results are obtained to demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this paper, a novel strategy for designing the heterogeneous tree multiplexer is proposed. The authors build the multiplexer delay model by curve fitting and then formulate the heterogeneous tree multiplexer design problem as a special type of optimization problem called mixed-integer nonlinear programming (MINLP). A new design parameter, the switch size in each stage, is introduced to improve the speed of the heterogeneous tree multiplexer. The proposed strategy can determine the multiplexer architecture and the switch size in each stage simultaneously. Three optimization methods are provided to synthesize the heterogeneous tree multiplexer according to the design specifications. [ABSTRACT FROM AUTHOR]

In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate \mathcal O(1/\lambda T) , but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain \mathcal O(\sqrt\log (1/\delta)/T) with $\delta$ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately \mathcal {O}(\log (\log (T)/\delta)/\lambda T) . [ABSTRACT FROM AUTHOR]

Nonlinear integer programming has not reached the same level of maturity as linear programming, and is still difficult to solve, especially for large-scale systems. Branch-and-bound ( B&B) and its variants are widely used methods for integer programming, and numerical solutions obtained by them still can be far away from the global optimum. In this paper, we propose a novel approach to guide the deterministic/heuristic methods and the commercial solvers for nonlinear integer programming, and aim at improving the solution quality by taking advantage of transformation under stability-retraining equilibrium characterization (TRUST-TECH) method. Moreover, we examine the effectiveness by developing and simulating TRUST-TECH guided B&B and TRUST-TECH guided commercial solver(s), and compare their performance with that of the original methods/solvers (e.g., GAMS (General Algebraic Modeling System)/ BARON, GAMS/SCIP, and LINDO (Linear, INteractive, Discrete Optimizer)/MINLP) and also with that of recently-reported evolutionary-algorithm (EA)-based methods. Simulation results provide evidence that, the solution quality is substantially improved, and the global-optimal solutions are usually obtained after the application of TRUST-TECH. The proposed approach can be immediately utilized to guide other EA-based methods and commercial solvers which incorporate intelligent searching components. [ABSTRACT FROM AUTHOR]

This work investigates the design of linear precoders in cooperative multi-cell MIMO downlink coverage for finite-alphabet source signals. Traditional design of multi-cell MIMO downlink precoder relies on Gaussian input assumption, which may lead to performance loss when true data inputs consist of discrete non-Gaussian symbols. This work presents optimized precoders for finite-alphabet input by maximizing the sum rate under per-base station power constraints. Specifically, we propose two distributed algorithms: a finite-alphabet signal Gaussian interference gradient projection algorithm and a block diagonalization alternating optimization algorithm while supporting interference cancellation. Our numerical results demonstrate considerable performance gain in terms of approximate transmission data rate as well as decoded bit error rate over precoding schemes designed by using the non-realistic Gaussian input assumption. [ABSTRACT FROM PUBLISHER]

This paper describes a hybrid numerical method with a combination of linear programming (LP) and nonlinear programming to design an asymmetric magnetic resonance imaging (MRI) system for head imaging. The method was successfully employed to a 0.7 T open-style MRI. Recently, we have updated the LP mathematical model in the hybrid optimization strategy by adding maximum axial and radial magnetic field strength limitation to make the magnet safe. The whole magnet consists of eight coaxial coils asymmetry about the z-axis. The six innermost coils are the main coils with positive current direction and the two outermost coils are shielding coils with negative current direction. All coils contribute their efforts to produce a central magnetic field strength of 3.0 T over a 20 cm diameter of spherical volume, and the peak to peak homogeneity is 12 ppm. The temperature bore for the magnet is formed with two different diameters cylindrical bores which makes head and shoulder access to the imaging volume easier. A 5 Gauss footprint area is restricted larger than an elliptical region with the sizes of 3.5 m in radial and 4.0 m in axial direction. The results show that the methodology is very flexible and efficient for asymmetric MRI magnet design. [ABSTRACT FROM PUBLISHER]

The statistical nature of gate delays in current day technologies necessitates the use of measures, such as path criticality and node/edge criticality for timing optimization. Node criticalities are typically computed using the complementary path delay. An alternative approach to compute the criticality using the circuit delay has been recently proposed. In this paper, we discuss in detail, the use of circuit delay to compute node criticalities and show that the criticality thus found is not equal to the conventional measure found using complementary path delay. However, there is a monotonic relationship between them and the two measures can be used interchangeably. We derive new bounds for the global criticality and propose a pruning algorithm based on these bounds to improve the accuracy and speed of computation. The use of this pruning technique results in a significant speedup in criticality computations. We obtain an order of magnitude average speedup for ISCAS benchmarks. [ABSTRACT FROM PUBLISHER]

Parallel transmission is a very promising candidate technology to mitigate the inevitable radio-frequency (RF) field inhomogeneity in magnetic resonance imaging at ultra-high field. For the first few years, pulse design utilizing this technique was expressed as a least squares problem with crude power regularizations aimed at controlling the specific absorption rate (SAR), hence the patient safety. This approach being suboptimal for many applications sensitive mostly to the magnitude of the spin excitation, and not its phase, the magnitude least squares (MLS) problem then was first formulated in 2007. Despite its importance and the availability of other powerful numerical optimization methods, the MLS problem yet has been faced almost exclusively by the pulse designer with the so-called variable exchange method. In this paper, we investigate various two-stage strategies consisting of different initializations and nonlinear programming approaches, and incorporate directly the strict SAR and hardware constraints. Several schemes such as sequential quadratic programming, interior point methods, semidefinite programming and magnitude squared least squares relaxations are studied both in the small and large tip angle regimes with RF and static field maps obtained in vivo on a human brain at 7T. Convergence and robustness of the different approaches are analyzed, and recommendations to tackle this specific problem are finally given. Small tip angle and inversion pulses are returned in a few seconds and in under a minute respectively while respecting the constraints, allowing the use of the proposed approach in routine. [ABSTRACT FROM PUBLISHER]

Nonnegative Matrix Factorization (NMF), a relatively novel paradigm for dimensionality reduction, has been in the ascendant since its inception. It incorporates the nonnegativity constraint and thus obtains the parts-based representation as well as enhancing the interpretability of the issue correspondingly. This survey paper mainly focuses on the theoretical research into NMF over the last 5 years, where the principles, basic models, properties, and algorithms of NMF along with its various modifications, extensions, and generalizations are summarized systematically. The existing NMF algorithms are divided into four categories: Basic NMF (BNMF), Constrained NMF (CNMF), Structured NMF (SNMF), and Generalized NMF (GNMF), upon which the design principles, characteristics, problems, relationships, and evolution of these algorithms are presented and analyzed comprehensively. Some related work not on NMF that NMF should learn from or has connections with is involved too. Moreover, some open issues remained to be solved are discussed. Several relevant application areas of NMF are also briefly described. This survey aims to construct an integrated, state-of-the-art framework for NMF concept, from which the follow-up research may benefit. [ABSTRACT FROM PUBLISHER]

Beamforming design for multi-user wireless relay networks under the criterion of maximin information throughput is an important but also very hard optimization problem due to its nonconvex nature. The existing approach to reformulate the design as a matrix rank-one constrained optimization problem is highly inefficient. This paper exploits the d.c. difference of two convex functions) structure of the objective function and the convex structure of the constraints in such a global optimization problem to develop efficient iterative algorithms of very low complexity to find the solutions. Both cases of concurrent and orthogonal transmissions from sources to relays are considered. Numerical results indicate that the proposed algorithms provide solutions that are very close to the upper bound on the solution of the non-orthogonal source transmissions case and are almost equal to the optimal solution of the orthogonal source transmissions case. This demonstrates the ability of the developed algorithms to locate approximations close to the global optimal solutions in a few iterations. Moreover, the proposed methods are superior to other methods in both performance and computation complexity. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of fair resource allocation based on the Nash bargaining solution (NBS) over wireless amplify-and-forward (AF) relay networks in which variable-rate users and constant-rate users coexist. Via the dual decomposition method, a distributed algorithm, including relay selection, relay power allocation, and rate adaptation, is proposed. When the number of users is large and/or the rate constraints are stringent, the problem becomes infeasible, and admission control is necessary. We formulate the admission control and fair resource allocation problem as a two-stage optimization problem, aiming to first find the maximal number of users the network can support with guaranteed rates and then allocate resources based on NBS for the admitted users. Since the problem is combinatorially hard, we transform it into an equivalent one-stage optimization problem, which can be solved by existing methods but with a higher computational complexity. To further reduce the computational complexity, a suboptimal algorithm is then developed. Simulations verify the convergence and fairness of the distributed algorithm. Simulations also show that the suboptimal algorithm achieves an approximate optimal performance but with a significant running time reduction. [ABSTRACT FROM PUBLISHER]

This paper presents a new method for VAr planning under multiple operating scenarios, optimizing sizes and locations of new reactive compensation equipment to ensure that both the system voltage profile and voltage stability requirements are met. The approach is based on \mbi L1-norm regularization for finding a solution with minimum VAr installation sites and on the \mbi L2-norm penalty function for satisfying the multiple state constraints. The \mbi L2-norm penalty function is exact in the sense that a finite penalty parameter is required to establish equivalence with the VAr planning problem, thus avoiding numerical ill-conditioning. The solution is obtained from a successive conic programming algorithm which makes use of adaptive trust-region control. The results of the new method are compared with those of an optimal power flow-based program for VAr planning. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problems of power allocation at relays to maximize the transmission rate in a nonorthogonal amplify-and-forward (AF) relay-assisted network, in which multiple relays simultaneously transmit the amplified signals in the same time and frequency. We first formulate a power allocation problem that aims to maximize the minimum of transmission rates in a multicast under a sum power constraint. Then, we prove that the optimal solution can be alternatively obtained by solving its inverse problem, which minimizes the sum power consumption at relays while achieving the target signal-to-noise ratio (SNR) with the same constraint. The inverse problem can be solved by using linear programming, and the inverse problem's complexity can be further reduced by a proposed suboptimal power allocation algorithm. As in the special case of multicast, the optimal solution is derived in unicast, and two suboptimal algorithms are proposed to reduce the feedback overhead and complexity, in which multirelay diversity is also investigated. [ABSTRACT FROM PUBLISHER]