Numerical nonlinear algebra is a computational paradigm that uses numerical analysis to study polynomial equations. Its origins were methods to solve systems of polynomial equations based on the classical theorem of B\'ezout. This was decisively linked to modern developments in algebraic geometry by the polyhedral homotopy algorithm of Huber and Sturmfels, which exploits the combinatorial structure of the equations and led to efficient software for solving polynomial equations. Subsequent growth of numerical nonlinear algebra continues to be informed by algebraic geometry and its applications. These include new approaches to solving, algorithms for studying positive-dimensional varieties, certification, and a range of applications both within mathematics and from other disciplines. With new implementations, numerical nonlinear algebra is now a fundamental computational tool for algebraic geometry and its applications. We survey some of these innovations and some recent applications.

When tasked with using limited resources to occupy an island chain, military leaders must decide how to distribute those resources in order to maximize some combination of objectives. To our knowledge, there has been no previous attempt to model such decisions using network optimization and/or integer programming. Thus, we model this problem as an integer program, where resources and geography specify constraints. Moreover, we introduce a novel modeling and analytical framework for studying the variability of the solutions under agendas varying between economic, military, and political objectives and use it to consider the robustness of the integer program in a real-world example.

A fundamental problem in algebraic geometry is to decompose the solution set of a polynomial system. A numerical irreducible decomposition is a numerical description of this solution set. Standard algorithms to compute this use a sequence of several homotopies. Our new approach uses isosingular theory and a classical result to compute a smooth point on every irreducible component in every dimension with a single homotopy. Key words. Numerical irreducible decomposition, isosingular sets, numerical algebraic geometry, intersection theory.

Biological systems are acknowledged to be robust to perturbations but a rigorous understanding of this has been elusive. In a mathematical model, perturbations often exert their effect through parameters, so sizes and shapes of parametric regions offer an integrated global estimate of robustness. Here, we explore this "parameter geography" for bistability in post-translational modification (PTM) systems. We use the previously developed "linear framework" for timescale separation to describe the steady-states of a two-site PTM system as the solutions of two polynomial equations in two variables, with eight non-dimensional parameters. Importantly, this approach allows us to accommodate enzyme mechanisms of arbitrary complexity beyond the conventional Michaelis-Menten scheme, which unrealistically forbids product rebinding. We further use the numerical algebraic geometry tools Bertini, Paramotopy, and alphaCertified to statistically assess the solutions to these equations at ∼109 parameter points in total. Subject to sampling limitations, we find no bistability when substrate amount is below a threshold relative to enzyme amounts. As substrate increases, the bistable region acquires 8-dimensional volume which increases in an apparently monotonic and sigmoidal manner towards saturation. The region remains connected but not convex, albeit with a high visibility ratio. Surprisingly, the saturating bistable region occupies a much smaller proportion of the sampling domain under mechanistic assumptions more realistic than the Michaelis-Menten scheme. We find that bistability is compromised by product rebinding and that unrealistic assumptions on enzyme mechanisms have obscured its parametric rarity. The apparent monotonic increase in volume of the bistable region remains perplexing because the region itself does not grow monotonically: parameter points can move back and forth between monostability and bistability. We suggest mathematical conjectures and questions arising from these findings. Advances in theory and software now permit insights into parameter geography to be uncovered by high-dimensional, data-centric analysis.

A common problem when analyzing models, such as mathematical modeling of a biological process, is to determine if the unknown parameters of the model can be determined from given input-output data. Identifiable models are models such that the unknown parameters can be determined to have a finite number of values given input-output data. The total number of such values over the complex numbers is called the identifiability degree of the model. Unidentifiable models are models such that the unknown parameters can have an infinite number of values given input-output data. For unidentifiable models, a set of identifiable functions of the parameters are sought so that the model can be reparametrized in terms of these functions yielding an identifiable model. In this work, we use numerical algebraic geometry to determine if a model given by polynomial or rational ordinary differential equations is identifiable or unidentifiable. For identifiable models, we present a novel approach to compute the identifiability degree. For unidentifiable models, we present a novel numerical differential algebra technique aimed at computing a set of algebraically independent identifiable functions. Several examples are used to demonstrate the new techniques.

The problem of geolocation of a transmitter via time difference of arrival (TDOA) and frequency difference of arrival (FDOA) is given as a system of polynomial equations. This allows for the use of homotopy continuation-based methods from numerical algebraic geometry. A novel geolocation algorithm employs numerical algebraic geometry techniques in conjunction with the random sample consensus (RANSAC) method. This is all developed and demonstrated in the setting of only FDOA measurements, without loss of generality. Additionally, the problem formulation as polynomial systems immediately provides lower bounds on the number of receivers or measurements required for the solution set to consist of only isolated points.

Bertini_real is a compiled command line program for numerically decomposing the real portion of a positive-dimensional complex component of an algebraic set. The software uses homotopy continuation to solve a series of systems via regeneration from a witness set to compute a cell decomposition. The implemented decomposition algorithms are similar to the well-known cylindrical algebraic decomposition (CAD) first established by Collins in that they produce a set of connected cells. In contrast to the CAD, Bertini_real produces cells with midpoints connected to boundary points by homotopies, which can easily be numerically tracked. Furthermore, the implemented decomposition for surfaces naturally yields a triangulation. This CAD-like decomposition captures the topological information and permits further computation on the real sets, such as sampling, visualization, and three-dimensional printing.

Given a polynomial system f, this article provides a general construction for homotopies that yield at least one point of each connected component on the set of solutions of $$f = 0$$ . This algorithmic approach is then used to compute a superset of the isolated points in the image of an algebraic set which arises in many applications, such as computing critical sets used in the decomposition of real algebraic sets. An example is presented which demonstrates the efficiency of this approach.

Homotopy continuation is a numerical method rooted in numerical linear algebra. When paired with some theory from algebraic geometry, it provides a means for approximating solutions of polynomial systems of equations. The linear algebra at the core of homotopy continuation involves solving linear systems of equations built from the Jacobian matrix of the polynomial system. Ill-conditioning of the Jacobian matrix thus causes either a dangerous loss of accuracy or a significant computational cost penalty from using adaptive precision methods. This article introduces a novel method for detecting some zones of ill-conditioning and for building piecewise-linear homotopy paths that avoid these ill-conditioned zones.

Bertini_real is a compiled command line program for numerically decomposing the real portion of a positive-dimensional complex component of an algebraic set. The software uses homotopy continuation to solve a series of systems via regeneration from a witness set to compute a cell decomposition. The implemented decomposition algorithms are similar to the well-known cylindrical algebraic decomposition (CAD) first established by Collins in that they produce a set of connected cells. In contrast to the CAD, Bertini_real produces cells with midpoints connected to boundary points by homotopies, which can easily be numerically tracked. Furthermore, the implemented decomposition for surfaces naturally yields a triangulation. This CAD-like decomposition captures the topological information and permits further computation on the real sets, such as sampling, visualization, and three-dimensional printing.

Bertini_real is a compiled command line program for numerically decomposing the real portion of a positive-dimensional complex component of an algebraic set. The software uses homotopy continuation to solve a series of systems via regeneration from a witness set to compute a cell decomposition. The implemented decomposition algorithms are similar to the well-known cylindrical algebraic decomposition (CAD) first established by Collins in that they produce a set of connected cells. In contrast to the CAD, Bertini_real produces cells with midpoints connected to boundary points by homotopies, which can easily be numerically tracked. Furthermore, the implemented decomposition for surfaces naturally yields a triangulation. This CAD-like decomposition captures the topological information and permits further computation on the real sets, such as sampling, visualization, and three-dimensional printing.

An efficient technique for solving polynomial systems with a particular structure is presented. This structure is very specific but arises naturally when computing the critical points of a symmetric polynomial energy function. This novel numerical solution method is based on homotopy continuation and is a particular application of results due to Canny and Rojas. An illustrative example from magnetism is presented.

Bertini real is a compiled command line program for numerically decomposing the real portion of a positive-dimensional complex component of an algebraic set. The software uses homotopy continuation to solve a series of systems via regeneration from a witness set to compute a cell decomposition. The implemented decomposition algorithms are similar to the well-known cylindrical algebraic decomposition (CAD) first established by Collins, in that they produce a set of connected cells. In contrast to the CAD, Bertini real produces cells with midpoints connected to boundary points by homotopies, which can easily be numerically tracked. Furthermore, the implemented decomposition for surfaces naturally yields a triangulation. This CAD-like decomposition captures the topological information and permits further computation on the real sets, such as sampling, visualization, and 3D printing.

Cooperating robotic systems, especially in the context of fault-tolerance of complex robotic mechanisms, is an important question for theoretical and applied studies. In this paper, we focus on one measure of fault tolerance in robots, namely, the multiplicity of the configurations for reaching a particular point in the workspace, which is difficult to measure using traditional methods. As a particular example, we consider the case of a free-swinging failure of a robotic arm that is handled by having a cooperating functional robot grasp the link adjacent to the failed joint. We present an efficient method to compute the multiplicity measure of the workspace, based on the tools from numerical algebraic geometry, applied to the inverse kinematics problem re-cast in the form of a polynomial system. To emphasize the difference between our methods and more traditional approaches, we compute the measure of workspace based on the multiplicity of configurations, and optimize placement of synergistic robot arms and the optimal grasp point on each link of the broken robot based on this measure.

We give a Descartes'-like bound on the number of positive solutions to a system of fewnomials that holds when its exponent vectors are not in convex position and a sign condition is satisfied. This was discovered while developing algorithms and software for computing the Gale transform of a fewnomial system, which is our main goal. This software is a component of a package we are developing for Khovanskii-Rolle continuation, which is a numerical algorithm to compute the real solutions to a system of fewnomials.

A MATLAB interface to the numerical homotopy continuation package Bertini is described. Bertini solves systems of polynomial equations. BertiniLab can be used to create input files for Bertini, run Bertini and process the solutions. All features of Bertini 1.5 are supported. The user can define the system of equations using a MATLAB numerical function, and vector and matrix operations are allowed. An object-oriented design allows the user to separate the statement of the problem from the details of the solution; the user can create subclasses to provide shortcuts or to tailor BertiniLab to a specific kind of problem. A complete example of an application to ferromagnetism is presented.

Researchers working with mathematical models are often confronted by the related problems of parameter estimation, model validation and model selection. These are all optimization problems, well known to be challenging due to nonlinearity, non-convexity and multiple local optima. Furthermore, the challenges are compounded when only partial data are available. Here, we consider polynomial models (e.g. mass-action chemical reaction networks at steady state) and describe a framework for their analysis based on optimization using numerical algebraic geometry. Specifically, we use probability-one polynomial homotopy continuation methods to compute all critical points of the objective function, then filter to recover the global optima. Our approach exploits the geometrical structures relating models and data, and we demonstrate its utility on examples from cell signalling, synthetic biology and epidemiology.

Bertini real is a compiled command line program for numerically decomposing the real portion of a positive-dimensional complex component of an algebraic set. The software uses homotopy continuation to solve a series of systems via regeneration from a witness set to compute a cell decomposition. This decomposition captures the topological information and permits further computation on the real sets, such as sampling, visualization, and 3D printing.