General Constraints Research Articles

Stress-activated constraints in dense suspension rheology

Dispersing small particles in a liquid can produce surprising behaviors when the solids fraction becomes large: rapid shearing drives these systems out of equilibrium and can lead to dramatic increases in viscosity (shear-thickening) or even solidification (shear jamming). These phenomena occur above a characteristic onset stress when particles are forced into frictional contact. Here we show via simulations how this can be understood within a framework that abstracts details of the forces acting at particle-particle contacts into general stress-activated constraints on relative particle movement. We find that focusing on just two constraints, affecting sliding and rolling at contact, can reproduce the experimentally observed shear thickening behavior quantitatively, despite widely different particle properties, surface chemistries, and suspending fluids. Within this framework parameters such as coefficients of sliding and rolling friction can each be viewed as a proxy for one or more forces of different physical or chemical origin, while the parameter magnitudes indicate the relative importance of the associated constraint. In this way, a new link is established that connects features observable in macroscale rheological measurements to classes of constraints arising from micro- or nano-scale properties.

Result diversification by multi-objective evolutionary algorithms with theoretical guarantees

Given a ground set of items, the result diversification problem aims to select a subset with high “quality” and “diversity” while satisfying some constraints. It arises in various real-world artificial intelligence applications, such as web-based search, document summarization and feature selection, and also has applications in other areas, e.g., computational geometry, databases, finance and operations research. Previous algorithms are mainly based on greedy or local search. In this paper, we propose to reformulate the result diversification problem as a bi-objective maximization problem, and solve it by a multi-objective evolutionary algorithm (EA), i.e., the GSEMO. We theoretically prove that the GSEMO can achieve the (asymptotically) optimal theoretical guarantees under both static and dynamic environments. For cardinality constraints, the GSEMO can achieve the optimal polynomial-time approximation ratio, 1/2. For more general matroid constraints, the GSEMO can achieve an asymptotically optimal polynomial-time approximation ratio, 1/2−ϵ/(4n), where ϵ>0 and n is the size of the ground set of items. Furthermore, when the objective function (i.e., a linear combination of quality and diversity) changes dynamically, the GSEMO can maintain this approximation ratio in polynomial running time, addressing the open question proposed by Borodin et al. [7]. This also theoretically shows the superiority of EAs over local search for solving dynamic optimization problems for the first time, and discloses the robustness of the mutation operator of EAs against dynamic changes. Experiments on the applications of web-based search, multi-label feature selection and document summarization show the superior performance of the GSEMO over the state-of-the-art algorithms (i.e., the greedy algorithm and local search) under both static and dynamic environments.

Reliable Contact Simulation With IPC.

Simulating the contact of deformable and possibly thin solids in a robust, accurate, and efficient manner is challenging. Traditionally, the contact of solids is approximately modeled with linearized geometric information near the contacting regions. This approximation is prone to generating underconstrained or overconstrained subproblems that can produce interpenetrating or numerically unstable results, especially when large deformation of solids is also present. To avoid these issues, we propose incremental potential contact (IPC) by formulating a mathematically consistent and general noninterpenetration constraint based on precisely calculated unsigned distances between boundary elements. IPC applies a customized barrier potential to directly relate the distances to the contact forces, which can grow infinitely large as the distance approaches zero to guarantee noninterpenetration. Results show reliable contact simulation even with versatile materials, large timestep sizes, fast impact velocities, severe deformation, and varying boundary conditions-bringing the intricate and important dynamical details to computer graphics in a reliable way for the first time.

A fast proximal iteratively reweighted nuclear norm algorithm for nonconvex low-rank matrix minimization problems

In this paper, we propose a fast proximal iteratively reweighted nuclear norm algorithm with extrapolation for solving a class of nonconvex low-rank matrix minimization problems. The proposed method incorporates two different extrapolation steps with respect to the previous iterations into the backward proximal step and the forward gradient step of the classic proximal iteratively reweighted method. We prove that the proposed method generates a convergent subsequence under general parameter constraints, and that any limit point is a stationary point of the problem. Furthermore, we prove that if the objective function satisfies the Kurdyka-Łojasiewicz property, the algorithm is globally convergent to a stationary point of the considered problem. Finally, we perform numerical experiments on a practical matrix completion problem with both synthetic and real data, the results of which demonstrate the efficiency and superior performance of the proposed algorithm.

Generalized Multiphase Dynamic Modeling and Precision Interaction Force Control of a Walking Lower Limb Hydraulic Exoskeleton.

Wearable lower limb hydraulic exoskeletons can be used to augment the human performance in heavy load transportation. Nonlinear and walking phase-dependent dynamics make the lower limb hydraulic exoskeleton become difficult to be modeled. This paper presents a generalized multiphase dynamic modeling method in which the dynamic model of each walking phase can all be solved based on a general higher dimensional dynamic model and different holonomic constraints. Compared to traditional lower limb exoskeleton modeling methods where the modeling of each walking phase is done independently, the proposed method is simple and applicable to arbitrary walking phases, especially for double leg support phase (closed-chain dynamics). Based on the established dynamic models, MIMO adaptive robust cascade force controllers (ARCFC) are designed both for double leg support phase and single leg support phase to effectively address high-order nonlinearities and various modeling uncertainties in hydraulic exoskeletons. An additional torque allocation method is proposed to deal with the overactuated characteristic in double leg support. Comparative simulations are conducted to verify the excellent human-machine interaction force control performance of the proposed scheme.

Assessment and numerical validation of a normal mode stability analysis for conjugate heat transfer

This paper aims to evaluate and validate a mathematical coupling model, based on a normal mode stability analysis, for steady conjugate heat transfer problems. In order to achieve this goal, an aerothermal computational code was designed specifically ex novo to meet the needs of the current study. A large number of numerical simulations were carried out within the framework of the Dirichlet-Robin fluid-solid interface procedure and with conditions strictly identical to those adopted in the mathematical model. These coupled computations have been synthesized in five representative cases. The computed results show that the stability limit and the numerical Biot number derived from the coupling model are very reliable parameters. Many other physical and numerical aspects, such as the splitting of the coupling period are also considered in this work from which some interesting and promising results are obtained. At the end of this paper, we assess the general potential, limits and constraints of the mathematical model and its extension to multidimensional cases is discussed.

Discrete interferences optimum beamformer in correlated signal and interfering noise

This paper introduces a significant special situation where the noise is a collection of D-plane interference signals and the correlated noise of D+1 is less than the number of array components. An optimal beamforming processor based on the minimum variance distortionless response (MVDR) generates and combines appropriate statistics for the D+1 model. Instead of the original space of the N-dimensional problem, the interference signal subspace is reduced to D+1. Typical antenna arrays in many modern communication networks absorb waves generated from multiple point sources. An analytical formula was derived to improve the signal to interference and noise ratio (SINR) obtained from the steering errors of the two beamformers. The proposed MVDR processor-based beamforming does not enforce general constraints. Therefore, it can also be used in systems where the steering vector is compromised by gain. Simulation results show that the output of the proposed beamformer based on the MVDR processor is usually close to the ideal state within a wide range of signal-to-noise ratio and signal-to-interference ratio. The MVDR processor-based beamformer has been experimentally evaluated. The proposed processor-based MVDR system significantly improves performance for large interference white noise ratio (INR) in the sidelobe region and provide an appropriate beam pattern.

Lifting symmetry breaking constraints with inductive logic programming

Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.

Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates

This work introduces the StoMADS-PB algorithm for constrained stochastic blackbox optimization, which is an extension of the mesh adaptive direct-search (MADS) method originally developed for deterministic blackbox optimization under general constraints. The values of the objective and constraint functions are provided by a noisy blackbox, i.e., they can only be computed with random noise whose distribution is unknown. As in MADS, constraint violations are aggregated into a single constraint violation function. Since all functions values are numerically unavailable, StoMADS-PB uses estimates and introduces so-called probabilistic bounds for the violation. Such estimates and bounds obtained from stochastic observations are required to be accurate and reliable with high but fixed probabilities. The proposed method, which allows intermediate infeasible iterates, accepts new points using sufficient decrease conditions and imposing a threshold on the probabilistic bounds. Using Clarke nonsmooth calculus and martingale theory, Clarke stationarity convergence results for the objective and the violation function are derived with probability one.

Bi-Matrix Games with General Intuitionistic Fuzzy Payoffs and Application in Corporate Environmental Behavior

Uncertainty is common in miscellaneous decision-making problems, including bi-matrix games. The uncertainty of bi-matrix games is caused by the complexity of the game environment and the limitations of players’ cognition rather than the asymmetry of information. Therefore, it is hard for players to precisely give their crisp payoff values. In this paper, a new method considering the acceptance degree that the general intuitionistic fuzzy constraints may be violated is developed to solve general intuitionistic fuzzy bi-matrix games (GIFBMGs). In the method, a new asymmetric general intuitionistic fuzzy number (GIFN) and its cut sets are firstly defined. Then, the order relationship of GIFNs and the definitions of α and β-bi-matrix games are proposed. Afterwards, the constructed general intuitionistic fuzzy quadratic program is converted into an interval bi-objective program on the basis of the order relationship of GIFNs. Furthermore, the interval bi-objective program is converted into a multi-objective quadratic program based on the combination of interval order relationship and the player’s acceptance degree. A goal programming approach is put forward to solve the multi-objective quadratic program. Finally, the validity of the proposed method is verified with a numerical example for corporate environmental behavior (CEB), and some comparative analyses are conducted to show the superiority of the proposed method.

A payoff dynamics model for generalized Nash equilibrium seeking in population games

This paper studies the problem of generalized Nash equilibrium seeking in population games under general affine equality and convex inequality constraints. In particular, we design a novel payoff dynamics model to steer the decision-making agents to a generalized Nash equilibrium of the underlying game, i.e., to a self-enforceable state where the constraints are satisfied and no agent has incentives to unilaterally deviate from her selected strategy. Moreover, using Lyapunov stability theory, we provide sufficient conditions to guarantee the asymptotic stability of the corresponding equilibria set in stable population games. Auxiliary results characterizing the properties of the equilibria set are also provided for general continuous population games. Furthermore, our theoretical developments are numerically validated on a Cournot game considering various market-related and production-related constraints.

An Atlas for the Inkjet Printing of Large-Area Tactile Sensors.

This review aims to discuss the inkjet printing technique as a fabrication method for the development of large-area tactile sensors. The paper focuses on the manufacturing techniques and various system-level sensor design aspects related to the inkjet manufacturing processes. The goal is to assess how printed electronics simplify the fabrication process of tactile sensors with respect to conventional fabrication methods and how these contribute to overcoming the difficulties arising in the development of tactile sensors for real robot applications. To this aim, a comparative analysis among different inkjet printing technologies and processes is performed, including a quantitative analysis of the design parameters, such as the costs, processing times, sensor layout, and general system-level constraints. The goal of the survey is to provide a complete map of the state of the art of inkjet printing, focusing on the most effective topics for the implementation of large-area tactile sensors and a view of the most relevant open problems that should be addressed to improve the effectiveness of these processes.

An Adaptive Approach for Impulsive Formation Maintenance Relevant to Distributed SAR Missions

This paper proposes an approach for impulsive formation maintenance tailored to distributed synthetic aperture radar, i.e., a spaceborne system composed by several antennas working together to provide enhanced remote sensing capabilities. The analyzed configuration is designed to guarantee the presence of a safety tube surrounding each satellite as the dynamics evolve. Formation requirements are related to general constraints on the acceptable along-track and radial/cross-track separations. The paper introduces an adaptive maintenance logic which fulfills these constraints. Specifically, the formation is adaptively redesigned around the chief every time geometry constraints are violated by means of a procedure developed by the authors in previous works and based on relative orbit parameters. Once these parameters are defined, the optimal impulsive burns required for orbit transfer are computed using state-of-the-art approaches. Performance in terms of delta-v and maneuver frequency is analyzed for a two-spacecraft formation exploiting a simulation environment based on MATLAB and GMAT. In ideal conditions, it is shown that maintenance costs are limited, while close proximity requires fine sensitivity on the applied impulses. A first assessment of the impact of relative navigation and maneuvering execution errors indicates that they play an important role in defining the overall control effort.

Adaptation and Exaptation: From Small Molecules to Feathers

Evolution works by adaptation and exaptation. At an organismal level, exaptation and adaptation are seen in the formation of organelles and the advent of multicellularity. At the sub-organismal level, molecular systems such as proteins and RNAs readily undergo adaptation and exaptation. Here we suggest that the concepts of adaptation and exaptation are universal, synergistic, and recursive and apply to small molecules such as metabolites, cofactors, and the building blocks of extant polymers. For example, adenosine has been extensively adapted and exapted throughout biological evolution. Chemical variants of adenosine that are products of adaptation include 2′ deoxyadenosine in DNA and a wide array of modified forms in mRNAs, tRNAs, rRNAs, and viral RNAs. Adenosine and its variants have been extensively exapted for various functions, including informational polymers (RNA, DNA), energy storage (ATP), metabolism (e.g., coenzyme A), and signaling (cyclic AMP). According to Gould, Vrba, and Darwin, exaptation imposes a general constraint on interpretation of history and origins; because of exaptation, extant function should not be used to explain evolutionary history. While this notion is accepted in evolutionary biology, it can also guide the study of the chemical origins of life. We propose that (i) evolutionary theory is broadly applicable from the dawn of life to the present time from molecules to organisms, (ii) exaptation and adaptation were important and simultaneous processes, and (iii) robust origin of life models can be constructed without conflating extant utility with historical basis of origins.

Finite curved creases in infinite isometric sheets.

Geometric stress focusing, e.g., in a crumpled sheet, creates pointlike vertices that terminate in a characteristic local crescent shape. The observed scaling of the size of this crescent is an open question in the stress focusing of elastic thin sheets. According to experiments and simulations, this size depends on the outer dimension of the sheet, but intuition and rudimentary energy balance indicate it should only depend on the sheet thickness. We address this discrepancy by modeling the observed crescent with a more geometric approach, where we treat the crescent as a curved crease in an isometric sheet. Although curved creases have already been studied extensively, the crescent in a crumpled sheet has its own unique features: the material crescent terminates within the material, and the material extent is indefinitely larger than the extent of the crescent. These features together with the general constraints of isometry lead to constraints linking the surface profile to the crease-line geometry. We construct several examples obeying these constraints, showing finite curved creases are fully realizable. This approach has some particular advantages over previous analyses, as we are able to describe the entire material without having to exclude the region around the sharp crescent. Finally, we deduce testable relations between the crease and the surrounding sheet and discuss some of the implications of our approach with regards to the scaling of the crescent size.

On Distributed Learning With Constant Communication Bits

In this paper, we study a distributed learning problem constrained by constant communication bits. Specifically, we consider the distributed hypothesis testing (DHT) problem where two distributed nodes are constrained to transmit a constant number of bits to a central decoder. In such settings, we show that in order to achieve the optimal error exponents, it suffices to consider the empirical distributions of observed data sequences and encode them to the transmission bits. With such a coding strategy, we develop a geometric approach in the distribution spaces and establish an inner bound of error exponent regions. In particular, we show the optimal achievable error exponents and coding schemes for the following cases: (i) both nodes can transmit <inline-formula> <tex-math notation="LaTeX">$\log _{2}3$ </tex-math></inline-formula> bits; (ii) one of the nodes can transmit 1 bit, and the other node is not constrained; (iii) the joint distribution of the nodes are conditionally independent given one hypothesis. Furthermore, we provide several numerical examples for illustrating the theoretical results. Our results provide theoretical guidance for designing practical distributed learning rules, and the developed approach also reveals new potentials for establishing error exponents for DHT with more general communication constraints.

Explaining reversible discourse marker sequences: A case study of and and so

When discourse markers (DMs) occur in sequences of multiple markers, their linear order is generally quite rigid. Still, there are clear cases of reversible DM sequences. This article examines the reasons for DM reversibility and explores its implications for theories of DM sequencing. A corpus analysis of the English DMs so and and suggests that one reason for their reversibility is that the different orders achieve different effects. When so follows and (And so…) it more often marks the upcoming utterance as a result or conclusion (‘therefore’). When so precedes and (So and…) it is more often used to change the discourse topic. Crucially, both of these functions are part of so's known functional spectrum. This shows that functional accounts of DM syntax must be sensitive to DM polyfunctionality. Moreover, a close analysis of each marker's individual contribution to their cumulative effect as a sequence points to a general ordering principle: In DM sequences, the earlier DM constrains the interpretation of the later DM by providing a particular interpretive context for it. This explains why alternative sequence positions may go along with different functions. In this way, the temporality of spoken language acts as a general constraint on DM sequencing.

Joint flight scheduling and task allocation for secure data collection in UAV-aided IoTs

With the flexible mobility and deployment, Unmanned Aerial Vehicles (UAVs) have created a new dimension to provide data collection services for a large number of distributed wireless sensory devices. Unfortunately, the broadcast nature of wireless channels make it hard to guarantee the security of data transmission in Internet of Things (IoTs). In this paper, focusing on the scenario where potential eavesdroppers intend to intercept the data uploading of legitimate nodes, we take the advantages of the UAV technology and the physical-layer security approach to study the Secure Data Collection Problem (SDCP). We first explore the flight scheduling and task allocation, that is, determining which areas to be visited and who to be served by UAVs, to establish favorable communication channels. Then we set up the utility function with secrecy rates and non-negative budget constraints. Due to the coupling between the UAV scheduling and task allocation optimization variables in the SDCP, we construct a surrogate function to achieve decoupling and reformulate SDCP into the SDCP-m problem, which is analyzed and proved to be a nonnegative monotone submodular function subject to a general constraint. Finally, an efficient Cost–Benefit Greedy (CBG) algorithm, which is theoretically analyzed to seek the quantitative value of approximation ratio, is proposed. The simulation results show that the CBG algorithm not only outperforms benchmark algorithms in terms of the average secrecy rate and the iteration number, but also generates a nearly optimal trajectory.

Building Load Control Using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants

Aggregation of heating, ventilation, and air conditioning (HVAC) loads can provide reserves to absorb volatile renewable energy, especially solar photo-voltaic (PV) generation. In this paper, we decide HVAC control schedules under uncertain PV generation, using a distributionally robust chance-constrained (DRCC) building load control model under two typical ambiguity sets: the moment-based and Wasserstein ambiguity sets. We derive mixed integer linear programming (MILP) reformulations for DRCC problems under both sets. Especially, for the Wasserstein ambiguity set, we use the right-hand side (RHS) uncertainty to derive a more compact MILP reformulation than the commonly known MILP reformulations with big-M constants. All the results also apply to general individual chance constraints with RHS uncertainty. Furthermore, we propose an adjustable chance-constrained variant to achieve tradeoff between the operational risk and costs. We derive MILP reformulations under the Wasserstein ambiguity set and second-order conic programming (SOCP) reformulations under the moment-based set. Using real-world data, we conduct computational studies to demonstrate the efficiency of the solution approaches and the effectiveness of the solutions. Summary of Contribution: The problem studied in this paper is motivated by a building load control problem that uses the aggregation of heating, ventilation, and air conditioning (HVAC) loads as flexible reserves to absorb uncertain solar photovoltaic (PV) generation. The problem is formulated as distributionally robust chance-constrained (DRCC) programs with right-hand side (RHS) uncertainty. In addition, we propose a risk-adjustable variant of the DRCC programs, where the risk level, instead of being predetermined, is treated as a decision variable. The paper aims to provide tractable reformulations and solution algorithms for both the (general) DRCC and the (general) adjustable DRCC models with RHS uncertainty.

A computational study on QP problems with general linear constraints

A computational study on QP problems with general linear constraints