Continuous Constraint Satisfaction Problems Research Articles

On Classifying Continuous Constraint Satisfaction Problems

A continuous constraint satisfaction problem (CCSP) is a constraint satisfaction problem (CSP) with an interval domain $U \subset \mathbb{R}$. We engage in a systematic study to classify CCSPs that are complete of the Existential Theory of the Reals, i.e., ER-complete. To define this class, we first consider the problem ETR, which also stands for Existential Theory of the Reals. In an instance of this problem we are given some sentence of the form $\exists x_1, \ldots, x_n \in \mathbb{R} : \Phi(x_1, \ldots, x_n)$, where $\Phi$ is a well-formed quantifier-free formula consisting of the symbols $\{0, 1, +, \cdot, \geq, >, \wedge, \vee, \neg\}$, the goal is to check whether this sentence is true. Now the class ER is the family of all problems that admit a polynomial-time many-one reduction to ETR. It is known that NP $\subseteq$ ER $\subseteq$ PSPACE. We restrict our attention on CCSPs with addition constraints ($x + y = z$) and some other mild technical conditions. Previously, it was shown that multiplication constraints ($x \cdot y = z$), squaring constraints ($x^2 = y$), or inversion constraints ($x\cdot y = 1$) are sufficient to establish ER-completeness. We extend this in the strongest possible sense for equality constraints as follows. We show that CCSPs (with addition constraints and some other mild technical conditions) that have any one well-behaved curved equality constraint ($f(x,y) = 0$) are ER-complete. We further extend our results to inequality constraints. We show that any well-behaved convexly curved and any well-behaved concavely curved inequality constraint ($f(x,y) \geq 0$ and $g(x,y) \geq 0$) imply ER-completeness on the class of such CCSPs.

A GPU-Based Parallel Region Classification Method for Continuous Constraint Satisfaction Problems

Abstract Continuous constraint satisfaction is prevalent in many science and engineering fields. When solving continuous constraint satisfaction problems, it is more advantageous for practitioners to derive all feasible regions (i.e., the solution space) rather than a limited number of solution points, since these feasible regions facilitate design concept generation and design tradeoff evaluation. Several central processing unit (CPU)-based branch-and-prune methods and geometric approximation methods have been proposed in prior research to derive feasible regions for continuous constraint satisfaction problems. However, these methods have not been extensively adopted in practice, mainly because of their high computational expense. To overcome the computational bottleneck of extant CPU-based methods, this paper introduces a GPU-based parallel region classification method to derive feasible regions for continuous constraint satisfaction problems in a reasonable computational time. Using interval arithmetic, coupled with the computational power of GPU, this method iteratively partitions the design space into many subregions and classifies these subregions as feasible, infeasible, and indeterminate regions. To visualize these classified regions in the design space, a planar visualization approach that projects all classified regions into one figure is also proposed. The GPU-based parallel region classification method and the planar visualization approach are validated through two case studies about the bird function and the welded beam design. These case studies show that the method and the approach can solve the continuous constraint satisfaction problems and visualize the results effectively and efficiently. A four-step procedure for implementing the method and the approach in practice is also outlined.

Temporal Set Inversion for Animated Implicits

We exploit the temporal coherence of closed-form animated implicit surfaces by locally re-evaluating an octree-like discretization of the implicit field only as and where is necessary to rigorously maintain a global error invariant over time, thereby saving resources in static or slowly-evolving areas far from the motion where per-frame updates are not necessary. We treat implicit surface rendering as a special case of the continuous constraint satisfaction problem of set inversion, which seeks preimages of arbitrary sets under vector-valued functions. From this perspective, we formalize a temporally-coherent set inversion algorithm that localizes changes in the field by range-bounding its time derivatives using interval arithmetic. We implement our algorithm on the GPU using persistent thread scheduling and apply it to the scalar case of implicit surface and swept volume rendering where we achieve significant speedups in complex scenes with localized deformations like those found in games and modelling applications where interactivity is required and bounded-error approximation is acceptable.

A continuous constraint satisfaction problem for the rigidity transition in confluent tissues

Models of confluent tissues are built out of tessellations of the space (both in two and three dimensions) in which the cost function is constructed in such a way that individual cells try to optimize their volume and surface in order to reach a target shape. At zero temperature, many of these models exhibit a rigidity transition that separates two phases: a liquid phase and a solid (glassy) phase. This phenomenology is now well established but the theoretical understanding is still not complete. In this work we consider an exactly soluble mean field model for the rigidity transition which is based on an abstract mapping. We replace volume and surface functions by random non-linear functions of a large number of degrees of freedom forced to be on a compact phase space. We then seek for a configuration of the degrees of freedom such that these random non-linear functions all attain the same value. This target value is a control parameter and plays the role of the target cell shape in biological tissue models. Therefore we map the microscopic models of cells to a random continuous constraint satisfaction problem with equality constraints. We argue that at zero temperature, the rigidity transition corresponds to the satisfiability transition of the problem. We also characterize both the satisfiable (SAT) and unsatisfiable (UNSAT) phase. In the SAT phase, before reaching the rigidity transition, the zero temperature SAT landscape undergoes an replica symmetry breaking (RSB)/ergodicity breaking transition of the same type as the Gardner transition in amorphous solids. By solving the RSB equations we compute the SAT/UNSAT threshold and the critical behavior around it. In the UNSAT phase we also compute the average shape index as a function of the target one and we compare the thermodynamical solution of the model with the results of the numerical greedy minimization of the corresponding cost function.

Optimization of the dynamic transition in the continuous coloring problem

Random constraint satisfaction problems (CSPs) can exhibit a phase where the number of constraints per variable α makes the system solvable in theory on the one hand, but also makes the search for a solution hard, meaning that common algorithms such as Monte Carlo (MC) method fail to find a solution. The onset of this hardness is deeply linked to the appearance of a dynamical phase transition where the phase space of the problem breaks into an exponential number of clusters. The exact position of this dynamical phase transition is not universal with respect to the details of the Hamiltonian one chooses to represent a given problem. In this paper, we develop some theoretical tools in order to find a systematic way to build a Hamiltonian that maximizes the dynamic α d threshold. To illustrate our techniques, we will concentrate on the problem of continuous coloring, where one tries to set an angle x i ∈ [0; 2π] on each node of a network in such a way that no adjacent nodes are closer than some threshold angle θ, that is cos(x i − x j )⩽ cos θ. This problem can be both seen as a continuous version of the discrete graph coloring problem or as a one-dimensional version of the Mari–Krzakala–Kurchan model. The relevance of this model stems from the fact that continuous CSPs on sparse random graphs remain largely unexplored in statistical physics. We show that for sufficiently small angle θ this model presents a random first order transition and compute the dynamical, condensation and Kesten–Stigum transitions; we also compare the analytical predictions with MC simulations for values of θ = 2π/q, . Choosing such values of q allows us to easily compare our results with the renowned problem of discrete coloring.

Jamming in Multilayer Supervised Learning Models.

Critical jamming transitions are characterized by an astonishing degree of universality. Analytic and numerical evidence points to the existence of a large universality class that encompasses finite and infinite dimensional spheres and continuous constraint satisfaction problems (CCSP) such as the nonconvex perceptron and related models. In this Letter we investigate multilayer neural networks (MLNN) learning random associations as models for CCSP that could potentially define different jamming universality classes. As opposed to simple perceptrons and infinite dimensional spheres, which are described by a single effective field in terms of which the constraints appear to be one dimensional, the description of MLNN involves multiple fields, and the constraints acquire a multidimensional character. We first study the models numerically and show that similarly to the perceptron, whenever jamming is isostatic, the sphere universality class is recovered, we then write the exact mean-field equations for the models and identify a dimensional reduction mechanism that leads to a scaling regime identical to the one of spheres.

Constraint satisfaction mechanisms for marginal stability and criticality in large ecosystems.

We discuss a resource-competition model, which takes MacArthur's model as a platform, to unveil interesting connections with glassy features and jamming in high dimensions. This model, as first studied by Tikhonov and Monasson, presents two qualitatively different phases: a shielded phase, where a collective, self-sustained behavior emerges, and a vulnerable phase, where a small perturbation can destabilize the system and contribute to population extinction. We first present our perspective based on a strong similarity with continuous constraint satisfaction problems in their convex regime. Then, we discuss the stability analysis in terms of the computation of the leading eigenvalue of the Hessian matrix of the associated Lyapunov function. This computation allows us to efficiently distinguish between the two aforementioned phases and to relate high-dimensional critical ecosystems to glassy phenomena in the low-temperature regime.

Efficient exploration of the High Entropy Alloy composition-phase space

High Entropy Alloys (HEAs), Multi-principal Component Alloys (MCA), or Compositionally Complex Alloys (CCAs) are alloys that contain multiple principal alloying elements. While many HEAs have been shown to have unique properties, their discovery has been largely done through costly and time-consuming trial-and-error approaches, with only an infinitesimally small fraction of the entire possible composition space having been explored. In this work, the exploration of the HEA composition space is framed as a Continuous Constraint Satisfaction Problem (CCSP) and solved using a novel Constraint Satisfaction Algorithm (CSA) for the rapid and robust exploration of alloy thermodynamic spaces. The algorithm is used to discover regions in the HEA Composition-Temperature space that satisfy desired phase constitution requirements. The algorithm is demonstrated against a new (TCHEA1) CALPHAD HEA thermodynamic database. The database is first validated by comparing phase stability predictions against experiments and then the CSA is deployed and tested against design tasks consisting of identifying not only single phase solid solution regions in ternary, quaternary and quinary composition spaces but also the identification of regions that are likely to yield precipitation-strengthened HEAs.

Universality of the SAT-UNSAT (jamming) threshold in non-convex continuous constraint satisfaction problems

Random constraint satisfaction problems (CSP) have been studied extensively using statistical physics techniques. They provide a benchmark to study average case scenarios instead of the worst case one. The interplay between statistical physics of disordered systems and computer science has brought new light into the realm of computational complexity theory, by introducing the notion of clustering of solutions, related to replica symmetry breaking. However, the class of problems in which clustering has been studied often involve discrete degrees of freedom: standard random CSPs are random (aka disordered Ising models) or random coloring problems (aka disordered Potts models). In this work we consider instead problems that involve continuous degrees of freedom. The simplest prototype of these problems is the perceptron. Here we discuss in detail the full phase diagram of the model. In the regions of parameter space where the problem is non-convex, leading to multiple disconnected clusters of solutions, the solution is critical at the SAT/UNSAT threshold and lies in the same universality class of the jamming transition of soft spheres. We show how the critical behavior at the satisfiability threshold emerges, and we compute the critical exponents associated to the approach to the transition from both the SAT and UNSAT phase. We conjecture that there is a large universality class of non-convex continuous CSPs whose SAT-UNSAT threshold is described by the same scaling solution.

A Constraint Satisfaction Algorithm for the Generalized Inverse Phase Stability Problem

Researchers have used the (calculation of phase diagram) CALPHAD method to solve the forward phase stability problem of mapping from specific thermodynamic conditions (material composition, temperature, pressure, etc.) to the associated phase constitution. Recently, optimization has been used to solve the inverse problem: mapping specific phase constitutions to the thermodynamic conditions that give rise to them. These pointwise results, however, are of limited value since they do not provide information about the forces driving the point to equilibrium. In this paper, we investigate the problem of mapping a desirable region in the phase constitution space to corresponding regions in the space of thermodynamic conditions. We term this problem the generalized inverse phase stability problem (GIPSP) and model the problem as a continuous constraint satisfaction problem (CCSP). In this paper, we propose a new CCSP algorithm tailored for the GIPSP. We investigate the performance of the algorithm on Fe–Ti binary alloy system using ThermoCalc with the TCFE7 database against a related algorithm. The algorithm is able to generate solutions for this problem with high performance.

The Inverse Phase Stability Problem as a Constraint Satisfaction Problem: Application to Materials Design

In general, the forward phase stability problem consists of mapping thermodynamic conditions (e.g., composition, temperature, pressure) to corresponding equilibrium states. In this paper, we instead focus on the generalized inverse phase stability problem (GIPSP) that deals with mapping a set of phase constitutions to a set of corresponding thermodynamic conditions. Specifically, we define the GIPSP as mapping of sets of phase constitution definitions in a multidimensional phase constitution search space to corresponding ranges of thermodynamic conditions. Mathematically, the solution to the GIPSP corresponds to all solutions to a continuous constraint satisfaction problem (CCSP). We present novel algorithms combining computational thermodynamics, evolutionary computation, and machine learning to approximate solution sets to the GIPSP as a CCSP. Some preliminary examples demonstrating the algorithms are presented. Moreover, the implications of the proposed framework for the larger problem of materials design are discussed, and future work is suggested.

Rigorous filtering using linear relaxations

This paper presents rigorous filtering methods for continuous constraint satisfaction problems based on linear relaxations, designed to efficiently handle the linear inequalities coming from a linear relaxation of quadratic constraints. Filtering or pruning stands for reducing the search space of constraint satisfaction problems. Discussed are old and new approaches for rigorously enclosing the solution set of linear systems of inequalities, as well as different methods for computing linear relaxations. This allows custom combinations of relaxation and filtering. Care is taken to ensure that all methods correctly account for rounding errors in the computations. The methods are implemented in the GloptLab environment for solving quadratic constraint satisfaction problems. Demonstrative examples and tests comparing the different linear relaxation methods are also presented.

An interval partitioning algorithm for constraint satisfaction problems

We propose an efficient interval partitioning algorithm to solve the continuous constraint satisfaction problem (CSP). The method comprises a new dynamic tree search management system that also invokes local search in selected subintervals. This approach is compared with two classical tree search techniques and three other interval methods. We study some challenging kinematics problems for testing the algorithm. The goal in solving kinematics problems is to identify all real solutions of the system of equations defining the problem. In other words, it is desired to find all object positions and orientations that satisfy a coupled non-linear system of equations. The kinematics benchmarks used here arise in industrial applications.

A symbolic-interval inference approach for constraint satisfaction: implementation on kinematics applications

In this study, a general solution methodology is presented for continuous constraint satisfaction problems. The performance of the method is demonstrated on challenging kinematics problems where the goal is to identify all real solutions. The developed approach assumes a collaborative methodology that integrates interval partitioning (IP), symbolic-interval inference (SII, a novel subdivision direction selection method) and local search. The resulting methodology aims at reducing the search space and discarding infeasible subspaces effectively and reliably during the early stages of the search so that exact solutions can be identified faster by local methods. SII conducts parallel multi-variable bisection with the aim of achieving immediate reduction in total box infeasibility of the majority of sibling boxes generated in every partitioning iteration. Empirical results are obtained on seven kinematics applications and compared with results obtained by other symbolic-interval methods.

Constraint propagation on quadratic constraints

This paper considers constraint propagation methods for continuous constraint satisfaction problems consisting of linear and quadratic constraints. All methods can be applied after suitable preprocessing to arbitrary algebraic constraints. The basic new techniques consist in eliminating bilinear entries from a quadratic constraint, and solving the resulting separable quadratic constraints by means of a sequence of univariate quadratic problems. Care is taken to ensure that all methods correctly account for rounding errors in the computations. Various tests and examples illustrate the advantage of the presented method.

Efficient interval partitioning—Local search collaboration for constraint satisfaction

In this article, a cooperative solution methodology that integrates interval partitioning ( IP) algorithms with a local search, feasible sequential quadratic programming ( FSQP), is presented as a technique to enhance the solving of continuous constraint satisfaction problems (continuous CSP). FSQP is invoked using a special search tree management system developed to increase search efficiency in finding feasible solutions. In this framework, we introduce a new symbolic method for selecting the subdivision directions that targets immediate reduction of the uncertainty related to constraint infeasibility in child boxes. This subdivision method is compared against two previously established partitioning rules (also parallelized in a similar manner) used in the interval literature and shown to improve the efficiency of IP. Further, the proposed tree management system is compared with tree management approaches that are classically used in IP. The whole method is compared with published results of established symbolic-numeric methods for solving CSP on a number of state-of-the-art benchmarks.

Unsolvability of some optimization problems

We show that over unbounded domain no algorithm can be created for the integer programming, nonlinear equations, continuous global optimization and constraint satisfaction problems.

Complete search in continuous global optimization and constraint satisfaction

This survey covers the state of the art of techniques for solving general-purpose constrained global optimization problems and continuous constraint satisfaction problems, with emphasis on complete techniques that provably find all solutions (if there are finitely many). The core of the material is presented in sufficient detail that the survey may serve as a text for teaching constrained global optimization.After giving motivations for and important examples of applications of global optimization, a precise problem definition is given, and a general form of the traditional first-order necessary conditions for a solution. Then more than a dozen software packages for complete global search are described.A quick review of incomplete methods for bound-constrained problems and recipes for their use in the constrained case follows; an explicit example is discussed, introducing the main techniques used within branch and bound techniques. Sections on interval arithmetic, constrained propagation and local optimization are followed by a discussion of how to avoid the cluster problem. Then a discussion of important problem transformations follows, in particular of linear, convex, and semilinear (= mixed integer linear) relaxations that are important for handling larger problems.Next, reliability issues – centring on rounding error handling and testing methodologies – are discussed, and the COCONUT framework for the integration of the different techniques is introduced. A list of challenges facing the field in the near future concludes the survey.

Experimental complexity analysis of continuous constraint satisfaction problems

Continuous constraint satisfaction problems are at the core of many real-world applications, including planning, scheduling, control, and diagnosis of physical systems such as car, planes, and factories. Yet in order to be effective in such resource-constrained environments, constraint-based techniques must take into account the complexity of continuous constrained problems. In this paper, we study complexity phase transition phenomena of continuous constraint satisfaction problems (CSPs). First, we analyze three continuous constraint satisfaction formulations based on (discrete) 3-SAT problems, which have a strong relation between structure and search cost. Then, we propose a generic benchmarking model for comparing continuous CSPs and algorithms, and present two example problems based on sine functions. In our experiments, these problems are solved using both local and global search methods. Besides comparing the complexities of different continuous CSPs and search algorithms, we also connect these back to results from similar studies on SAT problems. In solving continuous 3-SAT and sine-based CSPs, we find that the median search cost is characterized by simple parameters such as the constraint-to-variable ratio and constraint tightness, and that discrete search algorithms such as GSAT have continuous counter-parts with similar behavior. Regarding local versus global search techniques for constraint solving, our results show that local search methods are more efficient for weakly constrained problems, whereas global search methods work better on highly constrained problems.