Zsolt Ugray

Scatter Search and Local NLP Solvers: A Multistart Framework for Global Optimization

The algorithm described here, called OptQuest/NLP or OQNLP, is a heuristic designed to find global optima for pure and mixed integer nonlinear problems with many constraints and variables, where all problem functions are differentiable with respect to the continuous variables. It uses OptQuest, a commercial implementation of scatter search developed by OptTek Systems, Inc., to provide starting points for any gradient-based local solver for nonlinear programming (NLP) problems. This solver seeks a local solution from a subset of these points, holding discrete variables fixed. The procedure is motivated by our desire to combine the superior accuracy and feasibility-seeking behavior of gradient-based local NLP solvers with the global optimization abilities of OptQuest. Computational results include 155 smooth NLP and mixed integer nonlinear program (MINLP) problems due to Floudas et al. (1999), most with both linear and nonlinear constraints, coded in the GAMS modeling language. Some are quite large for global optimization, with over 100 variables and 100 constraints. Global solutions to almost all problems are found in a small number of local solver calls, often one or two.

A Multistart Scatter Search Heuristic for Smooth NLP and MINLP Problems

The algorithm described here, called OptQuest/NLP or OQNLP, is a heuristic designed to find global optima for pure and mixed integer nonlinear problems with many constraints and variables, where all problem functions are differentiable with respect to the continuous variables. It uses OptQuest, a commercial implementation of scatter search developed by OptTek Systems, Inc., to provide starting points for a gradient-based local NLP solver. This solver seeks a local solution from a subset of these points, holding discrete variables fixed. The procedure is motivated by our desire to combine the superior accuracy and feasibility-seeking behavior of gradient-based local NLP solvers with the global optimization abilities of OptQuest. Computational results include 144 smooth NLP and MINLP problems due to Floudas et al, most with both linear and nonlinear constraints, coded in the GAMS modeling language. Some are quite large for global optimization, with over 100 variables and many constraints. Global solutions to almost all problems are found in a small number of NLP solver calls, often one or two.

A digital multiprocessor hardware accelerator board for cellular neural networks : CNN-HAC

Analogue realizations of neural networks are superior in speed. the hardware accelerator boards using catalogue programmable VLSI ICs represent a trade-off having higher reconfigurability and lower cost. This paper presents such a solution for a cellular neural network (CNN). The architecture of the present design (CNN-HAC) using four standard DSPs to calculate the transient response of a one-layer CNN containing (0.25–0.75) × 106 analogue neural cells (depending on the type of template) is presented. the architecture and also the design principles are independent of the number of processors. the actual design was made in the form of a PC add-on board. The global control unit, which connects the board to the host firmware and communicates control signals to/from the local control units of the DSPs, was realized mainly with EPLDs. A special correspondence between the virtual processing elements—calculating the time-discrete models of the analogue neural cells—and the physical ones is discussed in detail. It is realized in an architecture with a simple, two-directional interprocessor communication. This architecture can be ‘scaled down’ using faster processors, EPLDs and memories. the present version runs with 2 μs/cell/iteration speed.

Security and privacy issues in mobile learning

The emergence of innovative uses of mobile and wireless devices in mobile learning (m-learning) go beyond the convenience of combining electronic learning applications with the mobility of portable devices. Technologies, protocols and policies that provide security and privacy in networked applications have to be augmented and adjusted to take into account the new uses and requirements of m-learning. This article reviews recent developments in m-learning with a focus on privacy and security issues. Developers of m-learning applications and organisations that deploy them have to understand the vulnerabilities and plan to address them early on in order to avoid potential exploitation efforts.

Dynamic filters and randomized drivers for the multi-start global optimization algorithm MSNLP

We present results of extensive computational tests of (i) comparing dynamic filters (first mentioned in an earlier publication addressing a feasibility seeking algorithm) with static filters and (ii) stochastic starting point generators (‘drivers’) for a multi-start global optimization algorithm called MSNLP (Multi-Start Non-Linear Programming). We show how the widely used NLP local solvers CONOPT and SNOPT compare when used in this context. Our computational tests utilize two large and diverse sets of test problems. Best known solutions to most of the problems are obtained competitively, within 30 solver calls, and the best solutions are often located in the first ten calls. The results show that the addition of dynamic filters and new global drivers can contribute to the increased reliability of the MSNLP algorithmic framework.

Does the use of computer-aided learning tools affect learning performance?

Studies comparing the learning performance differences between traditional courses and courses using computer-aided learning tools frequently give conclusions with reservations, indicating the possibility of other underlying factors that have a greater effect on learning performance than simply the medium used for content delivery. We analysed a uniquely assembled dataset covering a large number of university courses to revisit the media/method debate in the context of the effect of computer-aided learning tools on learning performance. Our results show that factors such as course type (determined by the content delivery medium), course subject, course level and student age all impact learning performance.

Mega Data Value Creation: A Roadmap to Success

The age of data is upon us. By the end of 2020, digital data created, replicated, and consumed will reach a lofty 4 zettabytes (ZB), which is equivalent to 4 billion terabytes (Gantz & Reinsel, 2012). Even without the current and predicted data deluge, organizations are experiencing coping issues with the data they already collect (McAfee & Brynjolfsson, 2012). Add to this the ability to track customer movements with smart phones (Bertolucci, 2013a), reach 1 billion unique users (each month) on YouTube (Youtube, 2014), reach 1 billion users on Facebook, reach 0.5 billion users on Twitter, track customer preferences through embedded devices, garner insights from uploaded images and videos (Gantz & Reinsel, 2012), and gather oceans of data from cable boxes, retail checkout, credit card transactions, and call center logs (Nichols, 2013). The way data creation, consumption, and storage is increasing exponentially, the term BIG does not capture the size—Mega Data is a more appropriate term.1

Team projects in a Systems Analysis and Design class: elements of success

An essential part of many Systems Analysis and Design capstone courses is the development of operational, meaningful and real-world information systems. The development of such systems typically takes place in a small group setting to realistically simulate the future work environment of Management Information System (MIS) students. We investigated how previous work experience, time spent on the project, project complexity, and the relationships among project team members and between team members and project stakeholders affected the outcome of the system development projects. Our findings indicate that three factors: prior work experience in information technology; experience with project management; the ability to select relatively simple projects positively affected the project outcome. While difficulties among team members showed a negative impact on the outcome of the projects, difficulties in the relationships with the stakeholders and total time spent on the project did not have significant effects.