General Solver Research Articles

A real-time capable method for planning minimum energy trajectories for one degree-of-freedom mechatronic systems

The synthesis of optimal motion profiles has shown to be a successful and virtually inexpensive solution for enhancing energy efficiency of mechatronic systems. A typical application is the design of point-to-point motion profiles for one-degree-of-freedom mechatronic systems. This paper proposes a new method for designing minimum energy trajectories for servo-actuated systems. The problem is solved by exploiting the knowledge of the structure of the optimal solution. That allows to solve the motion design problem by solving a set of nonlinear equations and, if needed, some basic optimization procedures, formulated after some suitable continuity conditions.The herein proposed method applies to systems with and without energy regeneration capability, for maximum adaptability to most industrial applications. The method also handles jerk, acceleration, and velocity constraints, which are typical requirements in many practical applications. The number of equations and thereby the computational time depends on the number of active constraints and on whether negative power is dissipated or regenerated. Overall, the method results to be suitable for Real-Time applications, also in the most challenging case in which all the constraints are active and the system cannot regenerate negative electric power. The accuracy and the effectiveness of the planning method is tested numerically, by comparing the solution to the one obtained by a general purpose optimal control solver and then also experimentally using a lab prototype.

Real-time simulation for detailed wind turbine model based on heterogeneous computing

The deployment of sophisticated and costly large-capacity wind turbines continues to rise. It is urgent for precise real-time simulation to accurately identify and address potential problems. However, the existing solutions, which joint various simulation platforms, suffer from the disadvantages of complex architecture and high cost. This paper proposes a CPU-FPGA heterogeneous computing-based real-time simulation platform for detailed wind turbine model (DWTM). DWTM encompasses the turbine model (covering the inflow wind, aerodynamic, and mechanical dynamic), the electrical model (covering the electromagnetic transient), and the control system. The real-time operating system is introduced to guarantee the real-time performance of the turbine model and control system in the CPU. FPGA is used to perform real-time electrical model calculation. Furthermore, a general FPGA solver for electromagnetic transients is designed. It can accommodate various circuit topologies without the need for recompiling the FPGA code. Finally, the real-time simulation performance and accuracy are verified. The coupled dynamics between the turbine and electrical models are investigated under multiple power grid and wind conditions.

DRLFluent: A distributed co-simulation framework coupling deep reinforcement learning with Ansys-Fluent on high-performance computing systems

For active flow control (AFC), several frameworks have been developed to enable dynamic interactions between deep reinforcement learning (DRL) agents and computational fluids dynamics (CFD) environments. However, the highly coupled modules in the existing frameworks require massive development efforts to be applied to different flow scenarios. Moreover, these frameworks do not support applications to run flexibly on multiple nodes in high-performance computing (HPC) systems. Herein, we propose a new framework named as DRLFluent coupling an open-source DRL package with a well-developed general CFD solver, Ansys-Fluent. The distributed HPC deployment of DRLFluent is achieved by a broker architecture which enables the DRL client and CFD server written in different languages to communicate across different nodes via a pair of standardized interfaces. We have evaluated DRLFluent along with two other representative frameworks using a benchmark case in which a laminar flow around a circular cylinder (Re=100) is controlled by two synthetic jets. The strategy discovered in DRLFluent is capable of neutralizing the small asymmetry of the shedding vortices, yielding a higher drag reduction than the other two frameworks. When the number of parallel environments exceeds 20, DRLFluent exhibits the largest speedup among the frameworks evaluated. Capable of distributing workload across multiple nodes, DRLFluent is suitable for implementing DRL for AFC of complex flow scenarios which requires hundreds of cores or more. Benefitting from a loose coupling between DRL and CFD, the standardized interfaces and the user-friendly CFD solver, DRLFluent is readily applicable to a wide range of flow scenarios without laborious low-level programming efforts.

Types of mathematical tasks in lower secondary classrooms in Germany

AbstractTasks play an important role in mathematics education, as they provide opportunities for students to develop their competencies and to cognitively engage with the mathematical content. The potential for cognitive activation as a central feature of a mathematical task has been considered in numerous studies, mostly as a didactical analysis by means of existing classification systems. In this study, which has been carried out in the frame of the TEDS research program, a different approach is taken by which we aim to identify the cognitive demand level of tasks used in ordinary mathematics lessons. Thus, the correlation between general mathematical competencies and the cognitive mathematical activities required to solve the task was analyzed and common properties of groupings of tasks were empirically extracted. In detail, 2490 tasks from mathematics lessons in Germany were analyzed by means of a rational task analysis with regard to their potential for fostering general mathematical competencies, namely modelling, problem solving, reasoning and argumentation, use of representations, use of symbols and operations, and communication. Latent class analysis revealed six classes of mathematical tasks with varying potential for the different competencies. In accordance with previous studies on mathematical tasks in Germany, the biggest class focused solely on the use of symbols and operations, while other classes showed different foci. Post hoc analyses revealed that the classes of tasks differ with regard to the level of cognitive activity they require. The results of the study highlight that the potential for cognitive activation of the tasks used in the classrooms of this more recent study has not improved in the last decades, despite many reform activities in German mathematics education, and that many mathematical tasks used are still more or less calculation oriented.

Formulation and solution technique for agricultural waste collection and transport network design

Agricultural waste management in developing countries has become a challenging issue for rural planners due to the lack of an efficient planning tool. In the countries, farmers burnt agricultural waste at fields after each harvesting season to solve the issue. As a result, it has caused air and water pollution in the rural areas of the countries. In this paper, we present a mixed-integer nonlinear programming model for agricultural waste collection and transport network design that aims to stop burning waste and use the waste to produce bio-organic fertilizer. The model supports rural planners to optimally locate waste storages, and to determine the optimal set of routes for a fleet of vehicles to collect and transport the waste from the storages to the bio-organic fertilizer production facility. In the novel location-assignment-routing problem, the overall objective is to minimize total cost of locating storages, collecting waste from fields and planning vehicle routes. A solution technique is developed to linearise the mixed-integer nonlinear programming model into a model in linear form. In addition, a parallel water flow algorithm is developed to solve efficiently the large-sized instances. The efficiency of the proposed model and algorithm is validated and evaluated on the real case study in Trieu Phong district, Quang Tri province, Vietnam, as well as a set of randomly generated large-sized instances. The results show that our solution approach outperforms the general optimisation solver and tabu search algorithm. Our algorithm can find the optimal or near-optimal solutions for the large-sized instances within a reasonable time.

Research and Design of Planning Systems in the AORBCO Model

Abstract Planning research in artificial intelligence began earlier, dating back to 1969, and the general problem solver (GPS) designed by Newell and Simon [1]. Since then, other researchers have also proposed other planners, such as QA3 system using theorem proving method to construct planning[2], Fikes and Nilsson designed STRIPS planning system [3], until today STRIPS planning method is still only a very important research method in the field of planning. After more than 50 years of research, there have been many intelligent planning methods such as graph planning method, probability planning, timing planning, heuristic planning and perceptual planning. Although the existing intelligent planning methods have been able to solve some problems, the existing planning mainly focuses on the design of the planning algorithm, and the manual analysis preprocessing workload is large. Research has pointed out that the new generation of intelligent planners should be knowledge-based development of knowledge-based planning systems is a meaningful work [4]. Therefore, by analyzing the existing intelligent planning methods and AORBCO model, this paper gives the definition of intelligent planning with self-consciousness, and proposes an intelligent planning method based on self-consciousness based on behavior control.

The Poetry and Music of Science: Comparing Creativity in Science and Art

The Poetry and Music of Science: Comparing Creativity in Science and Art

On relaxations of the max k-cut problem formulations

A tight continuous relaxation is a crucial factor in solving mixed integer formulations of many NP-hard combinatorial optimization problems. The (weighted) max k-cut problem is a fundamental combinatorial optimization problem with multiple notorious mixed integer optimization formulations. In this paper, we explore four existing mixed integer optimization formulations of the max k-cut problem. Specifically, we show that the continuous relaxation of a binary quadratic optimization formulation of the problem is: (i) stronger than the continuous relaxation of two mixed integer linear optimization formulations and (ii) at least as strong as the continuous relaxation of a mixed integer semidefinite optimization formulation. We also conduct a set of experiments on multiple sets of instances of the max k-cut problem using state-of-the-art solvers that empirically confirm the theoretical results in item (i). Furthermore, these numerical results illustrate the advances in the efficiency of global non-convex quadratic optimization solvers and more general mixed integer nonlinear optimization solvers. As a result, these solvers provide a promising option to solve combinatorial optimization problems. Our codes and data are available on GitHub.

Optimized matrix ordering of sparse linear solver using a few-shot model for circuit simulation

The sparse linear solver has become the bottleneck in a SPICE-like circuit simulator. A general sparse linear solver comprises pre-analysis, numeric factorization, and right-hand solving. The matrix ordering method in pre-analysis determines fill-ins and matrix structure, which is critical for the performance of numeric factorization and right-hand solving. Current leading solvers use a sequential (e.g., KLU) or completely parallel (e.g., NICSLU) framework to order the matrix, which produces non-optimal options or consumes tremendous memory. Furthermore, the criterion for judging the optimal ordering method is also controversial. In this paper, we propose a new pre-analysis framework. It redefines the criterion of the optimal ordering method and utilizes a few-shot model for inference. By performing up to 2 ordering methods from the proposed pre-analysis, our work can implement an optimal ordering method and consume much less memory than a completely parallel framework. Experimental results on 70 circuit matrices show that compared to KLU, our work achieves an average time improvement of 27.09%. Compared to NICSLU, our work achieves an average time improvement of 14.39% and an average memory improvement of 61.52% in the sequential mode, and an average time improvement of 15.74% and an average memory improvement of 55.11% in the parallel (16 threads) mode.

Tourism Sector Development Strategy Of Kalimantung Island And Mursala Island In Islamic Economic Perspective

One of the sectors that can support the increase in Regional Original Revenue (PAD), improvement of social life of the community and improvements to the marine environment is the tourism sector. However, the tourism sector in Central Tapanuli Regency has not been managed and utilized optimally, especially the tourism of kalimantung island and mursala island. The author focuses on the problem of the development strategy of the tourism sector of kalimantung island and mursala island and the many tourism assets in Central Tapanuli Regency that are still not managed properly and correctly, which is due to the lack of cooperative relations with related agencies, academics, business people, communities, and local media in building and developing tourist attractions, and the lack of community participation in developing tourist attractions. This research uses a qualitative method with a descriptive approach through SWOT analysis with the Penthahelix Model to describe the situation in the field which is then examined to find general problem solving. The purpose of this research is to find out the extent to which Stakeholders (Academics, Business Actors, Communities, Government and Media) participate in the development of tourism in the district of Centralapanuli especially on Kalimantung Island and Mursala Island along with phenomena that describe the obstacles faced and efforts to overcome obstacles.

A general forward solver for 3D CSEMs with multitype sources and operating environments

To determine the electromagnetic (EM) fields of different three-dimensional (3D) controlled-source electromagnetic methods (CSEMs) using the same parameters of the forward solution, by explicitly considering the commonalities, we present a general 3D forward modeling solver for CSEMs with multitype sources and operating environments. The commonality of the solver is reflected in two aspects. First, the solver is based on a frequency-domain (FD) vector Helmholtz equation for determining the scattered electric field. The different types of sources are imposed on the right-hand term of the equation, expressed as background Green’s function. Second, sources of any CSEM can be composed of electric dipole (ED) or magnetic dipole (MD) superposition. Thus, the focus of the 3D forward modeling of CSEMs is reduced to determining the EM fields of ED or MD sources for the background medium. The quasi-minimal residual (QMR) method is used to solve the large sparse complex linear system. Once the FD EM fields have been calculated, the time-domain (TD) response can be obtained using the cosine/sine transformation. The numerical results show that the relative error is less than 5% between the 3D numerical and analytical solutions, which verifies the accuracy of the solver. We further study the difference between the real (bent) and theoretical (straight) wires. We suggest that the shape of the source must be considered for TD and FD CSEMs with a wire source during data processing and inversion. The last example investigated the characteristics of FD EM fields from a finite-length wire and TD EM fields from a rectangular fixed loop on the same conductive tilted disk model buried in resistive sediments. According to the numerical results, we recommend FD CSEMs with a wire source for detecting deep anomalies.

Solving Domain-Independent Dynamic Programming Problems with Anytime Heuristic Search

Domain-independent dynamic programming (DIDP) is a recently proposed model-based paradigm for combinatorial optimization where a problem is formulated as dynamic programming (DP) and solved by a generic solver. In this paper, we develop anytime heuristic search solvers for DIDP, which quickly find a feasible solution and continuously improve it to prove optimality. We implement six anytime heuristic search algorithms previously used as problem-specific methods and evaluate them on nine different problem classes. Our experimental results show that most of the anytime DIDP solvers outperform an existing A*-based solver, mixed-integer programming, and constraint programming in proving optimality, solution quality, and primal integral across multiple problem classes. In particular, complete anytime beam search (CABS) performs the best, improving on the best-known solution for one instance of traveling salesman problem with time windows and closing five instances of one-to-one multi-commodity pick-and-delivery traveling salesman problems.

Domain-Independent Dynamic Programming: Generic State Space Search for Combinatorial Optimization

For combinatorial optimization problems, model-based approaches such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a problem: the `holy grail' of declarative problem solving. We propose domain-independent dynamic programming (DIDP), a new model-based paradigm based on dynamic programming (DP). While DP is not new, it has typically been implemented as a problem-specific method. We propose Dynamic Programming Description Language (DyPDL), a formalism to define DP models, and develop Cost-Algebraic A* Solver for DyPDL (CAASDy), a generic solver for DyPDL using state space search. We formalize existing problem-specific DP and state space search methods for combinatorial optimization problems as DP models in DyPDL. Using CAASDy and commercial MIP and CP solvers, we experimentally compare the DP models with existing MIP and CP models, showing that, despite its nascent nature, CAASDy outperforms MIP and CP on a number of common problem classes.

The Impact of Family Functioning on Help-Seeking Behavior and Symptom Severity in Obsessive-Compulsive Disorder.

Poor family functioning is associated with higher symptom severity in pediatric obsessive-compulsive disorder (OCD) and delayed help-seeking behavior in other forms of psychopathology. However, little is known about the impact of family functioning on help-seeking behavior and symptom severity in adults with OCD. The present study investigated the association between family functioning and both treatment delay and symptom severity in adults with obsessive-compulsive symptoms. Participants were 194 adults who self-identified as having OCD and completed an internet survey, including measures assessing family functioning, obsessive-compulsive symptom severity, help-seeking behavior, and depression symptom severity. Poorer family functioning was associated with higher obsessive-compulsive and depression symptom severity, after controlling for significant demographic variables. With respect to domains of family functioning, poorer general functioning, problem solving, communication skills, role functioning, affective involvement, and affective responsiveness were associated with higher obsessive-compulsive and depression symptom severity, after controlling for demographics. Poorer problem solving and communication were not significantly associated with treatment delay after controlling for demographics. Findings highlight the need for family intervention within the treatment framework for adult OCD and suggest targets ( e.g. , communication) to be addressed.

Joint location and cost planning in maximum capture facility location under random utilities

We study a joint facility location and cost planning problem in a competitive market under random utility maximization (RUM) models. The objective is to locate new facilities and make decisions on the costs (or budgets) to spend on the new facilities, aiming to maximize an expected captured customer demand, assuming that customers choose a facility among all available facilities according to a RUM model. We examine two RUM frameworks in the discrete choice literature, namely, the additive and multiplicative RUM. While the former has been widely used in facility location problems, we are the first to explore the latter in the context. We numerically show that the two RUM frameworks can well approximate each other in the context of the cost optimization problem. In addition, we show that, under the additive RUM framework, the resultant cost optimization problem becomes highly non-convex and may have several local optima. In contrast, the use of the multiplicative RUM brings several advantages to the competitive facility location problem. For instance, the cost optimization problem under the multiplicative RUM can be solved efficiently by a general convex optimization solver, or can be reformulated as a conic quadratic program and handled by a conic solver available in some off-the-shelf solvers such as CPLEX or GUROBI. Furthermore, we consider a joint location and cost optimization problem under the multiplicative RUM and propose three approaches to solve the problem, namely, an equivalent conic reformulation, a multi-cut outer-approximation algorithm, and a local search heuristic. We provide numerical experiments based on synthetic instances of various sizes to evaluate the performances of the proposed algorithms in solving the cost optimization and the joint location and cost optimization problems.

RAFA: Redundancies-assisted Algebraic Fault Analysis and its implementation on SPN block ciphers

Algebraic Fault Analysis (AFA) is a cryptanalysis for block ciphers proposed by Courtois et al., which incorporates algebraic cryptanalysis to overcome the complexity of manual analysis within the context of Differential Fault Analysis (DFA). The effectiveness of AFA on lightweight block ciphers has been demonstrated. However, the complexity of the algebraic systems prevents it from attacking heavyweight block ciphers efficiently. In this paper, we propose a novel cryptanalysis called Redundancies-assisted Algebraic Fault Analysis (RAFA) to facilitate the solution of algebraic systems in the setting of heavyweight block ciphers. The core idea of RAFA is to expedite SAT solvers by modifying the algebraic systems, which is accomplished via two methods. The first method introduces redundant constraints, which is proposed for the first time in the context of algebraic cryptanalysis. The second one is a sophisticated linearization of the nonlinear Algebraic Normal Form (ANF). It takes RAFA for about 9.68 hours to attack AES-128. To the best of our knowledge, this is the first work that uses a general SAT solver to attack AES with only a single injection of byte-fault. Moreover, RAFA can attack AES-128 in 50.92 and 27.54 minutes for nibble- and bit-based fault model, respectively. In comparison, the traditional DFA algorithm implemented by pure C takes 4 ~ 5 hours under all three fault models investigated in this work. Moreover, in order to show the generality of RAFA, we also apply it to other heavyweight block ciphers. The best results show that RAFA could recover the key of Serpent-256 and SPEEDY-r-192 in 20.7 and 1.5 hours using only three faults, respectively. In comparison, AFA could not break these two ciphers even when 30 bits and 50 bits of their keys are known, respectively. Furthermore, no DFA work on Serpent or SPEEDY is known using comparable fault models.

On SOCP-based disjunctive cuts for solving a class of integer bilevel nonlinear programs

We study a class of integer bilevel programs with second-order cone constraints at the upper-level and a convex-quadratic objective function and linear constraints at the lower-level. We develop disjunctive cuts (DCs) to separate bilevel-infeasible solutions using a second-order-cone-based cut-generating procedure. We propose DC separation strategies and consider several approaches for removing redundant disjunctions and normalization. Using these DCs, we propose a branch-and-cut algorithm for the problem class we study, and a cutting-plane method for the problem variant with only binary variables. We present an extensive computational study on a diverse set of instances, including instances with binary and with integer variables, and instances with a single and with multiple linking constraints. Our computational study demonstrates that the proposed enhancements of our solution approaches are effective for improving the performance. Moreover, both of our approaches outperform a state-of-the-art generic solver for mixed-integer bilevel linear programs that is able to solve a linearized version of our binary instances.

Am I ready to graduate? Management majors’ perceived collegiate skill development

We examine 15 skills of graduating management majors as measured by self-rated perceptions of change during their college tenure. The sample includes 520,052 students from 697 higher education institutions in the United States. The research questions are: (1) As judged by management majors themselves, how much have their skills changed as a result of higher education? (2) How do these self-perceptions compare to other students? (3) What is the estimated impact of a management degree on these changes? Notable findings include that at least 90% of students reporting stronger, if not much stronger, skills in the categories of general knowledge, critical thinking, analytics, problem solving, and knowledge of their chosen field (management). However, other skills such as leadership are reported by 16% of management students as experiencing no change. The ratings of communication skills categories of public speaking and writing reported 17% of each category as no change or weaker skills is these areas. Study findings are discussed along with recommendations for future research.

GOPS: A general optimal control problem solver for autonomous driving and industrial control applications

Solving optimal control problems serves as the basic demand of industrial control tasks. Existing methods like model predictive control often suffer from heavy online computational burdens. Reinforcement learning has shown promise in computer and board games but has yet to be widely adopted in industrial applications due to a lack of accessible, high-accuracy solvers. Current Reinforcement learning (RL) solvers are often developed for academic research and require a significant amount of theoretical knowledge and programming skills. Besides, many of them only support Python-based environments and limit to model-free algorithms. To address this gap, this paper develops General Optimal control Problems Solver (GOPS), an easy-to-use RL solver package that aims to build real-time and high-performance controllers in industrial fields. GOPS is built with a highly modular structure that retains a flexible framework for secondary development. Considering the diversity of industrial control tasks, GOPS also includes a conversion tool that allows for the use of Matlab/Simulink to support environment construction, controller design, and performance validation. To handle large-scale problems, GOPS can automatically create various serial and parallel trainers by flexibly combining embedded buffers and samplers. It offers a variety of common approximate functions for policy and value functions, including polynomial, multilayer perceptron, convolutional neural network, etc. Additionally, constrained and robust algorithms for special industrial control systems with state constraints and model uncertainties are also integrated into GOPS. Several examples, including linear quadratic control, inverted double pendulum, vehicle tracking, humanoid robot, obstacle avoidance, and active suspension control, are tested to verify the performances of GOPS.

A three-dimensional solver for simulating detonation on curvilinear adaptive meshes

A generic solver in a parallel Cartesian adaptive mesh refinement framework is extended to simulate detonations on three-dimensional structured curvilinear meshes. A second-order accurate finite volume method is used with grid-aligned Riemann solvers for thermally perfect gas mixtures. Detailed, multi-step chemical kinetic mechanisms are employed and numerically incorporated with a splitting approach. The adaptive mesh refinement technique is applied to a mapped mesh using modified prolongation and restriction operators. The flux along the coarse-fine interface is considered in a correction procedure to ensure the conservation of the solver. The numerical accuracy, conservation and robustness of the simulations are verified and validated with suitable benchmark tests. The new solver is then used to simulate detonation problems in non-Cartesian geometries. A simulation is conducted of the three-dimensional detonation propagation in a 90-degree pipe bend. A detonation in a round tube is also simulated in a Galilean frame of reference. Both a rectangular mode and a spinning mode are observed in the simulations. In addition, the fundamental problem of detonation wave/boundary layer interaction is studied. The results show that the new solver can simulate high-speed reactive flows efficiently by the combined use of a curvilinear mapping with mesh adaptation.