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A Numerical Local Dimension Test for Points on the Solution Set of a System of Polynomial Equations

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A Numerical Local Dimension Test for Points on the Solution Set of a System of Polynomial Equations
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  A NUMERICAL LOCAL DIMENSION TEST FOR POINTS ON THESOLUTION SET OF A SYSTEM OF POLYNOMIAL EQUATIONS DANIEL J. BATES ∗ , JONATHAN D. HAUENSTEIN † , CHRIS PETERSON ‡ ,  AND ANDREW J. SOMMESE § Abstract.  The solution set  V    of a polynomial system, i.e., the set of common zeroes of a setof multivariate polynomials with complex coefficients, may contain several components, e.g., points,curves, surfaces, etc. Each component has attached to it a number of quantities, one of which isits dimension. Given a numerical approximation to a point  p  on the set  V   , this article presents anefficient algorithm to compute the maximum dimension of the irreducible components of   V    whichpass through  p , i.e., a local dimension test. Such a test is a crucial element in the homotopy-basednumerical irreducible decomposition algorithms of Sommese, Verschelde, and Wampler.Computational evidence presented in this article illustrates that the use of this new algorithmgreatly reduces the cost of so-called “junk-point filtering,” previously a significant bottleneck in thecomputation of a numerical irreducible decomposition. For moderate size examples, this results inwell over an order of magnitude improvement in the computation of a numerical irreducible decom-position. As the computation of a numerical irreducible decomposition is a fundamental  backbone  operation, gains in efficiency in the irreducible decomposition algorithm carry over to the many com-putations which require this decomposition as an initial step. Another feature of a local dimensiontest is that one can now compute the irreducible components in a prescribed dimension without firstcomputing the numerical irreducible decomposition of all higher dimensions. For example, one maycompute the isolated solutions of a polynomial system without having to carry out the full numericalirreducible decomposition. Keywords . local dimension, generic points, homotopy continuation, irreducible components, mul-tiplicity, numerical algebraic geometry, polynomial system, primary decomposition, algebraic set,algebraic variety AMS Subject Classification.  65H10, 68W30, 14Q99 Introduction.  The solution set  V   of a system of polynomial equations maycontain many pieces (irreducible components), e.g., points, curves, surfaces, etc., eachwith its own dimension. A fundamental problem when working with such systems isto describe the set of components (the irreducible decomposition) of the solution set(algebraic set, or variety). In some areas of application, it is sufficient to computeone or more isolated (zero-dimensional) solutions. However, there are times when itis necessary to have knowledge of the entire solution set, in all dimensions. One suchapplication is in the field of kinematics, where the dimensions of solution componentsindicate the number of degrees of freedom in various configurations of mechanisms [33].Recently, numerical homotopy methods have been developed to carry out thisdecomposition, resulting in a numerical irreducible decomposition. Given a point p  on at least one irreducible component, a key difficulty in these methods is the ∗ Department of Mathematics, Colorado State University, Fort Collins, CO 80523(bates@math.colostate.edu,  www.math.colostate.edu/ ∼ bates ). This author was supported by Col-orado State University and the Institute for Mathematics and Its Applications (IMA) † Department of Mathematics, University of Notre Dame, Notre Dame, IN 46556(jhauenst@nd.edu,  www.nd.edu/ ∼ jhauenst ). This author was supported by the Duncan Chair of the University of Notre Dame, the University of Notre Dame Center for Applied Mathematics, NSFgrant DMS-0410047, and NSF grant DMS-0712910 ‡ Department of Mathematics, Colorado State University, Fort Collins, CO 80523 (peter-son@math.colostate.edu,  www.math.colostate.edu/ ∼ peterson ). This author was supported by Col-orado State University, NSF grant MSPA-MCS-0434351, AFOSR-FA9550-08-1-0166, and the Insti-tute for Mathematics and Its Applications (IMA) § Department of Mathematics, University of Notre Dame, Notre Dame, IN 46556(sommese@nd.edu,  www.nd.edu/ ∼ sommese ). This author was supported by the Duncan Chair of the University of Notre Dame, NSF grant DMS-0410047, and NSF grant DMS-07129101  determination of the dimension(s) of the component(s) on which  p  sits. Severalalgorithms to compute this information have been suggested in recent years, butall have fundamental drawbacks. This paper provides a novel method that does notsuffer from these drawbacks.More technically, given a numerical approximation of a point  p  on the algebraicset  V   =  { x  ∈  C N  |  F  ( x ) = 0 }  for a set of polynomials  F   :=  { F  1 ( x ) ,...,F  n ( x ) } ⊂ C [ x 1 ,...,x N  ], it is of significant computational value to know the maximum dimen-sion of the irreducible components containing  p , called the local dimension of   p  anddenoted here as dim p ( V   ). This article presents a rigorous numerical local dimensiontest. The test, which is efficient and robust, is based on the theory of Macaulay[23] and, more specifically, on the numerical approach of Dayton and Zeng [8] forcomputing multiplicities.This new algorithm is valuable in a number of settings, several of which arediscussed in this article:1. determining whether a given solution is isolated (and computing the multi-plicity if it is);2. computing dim p ( V   ) for nonisolated points  p ;3. finding all irreducible components that contain a specified point  p ;4. computing the numerical irreducible decomposition of   V   more efficiently byreducing the junk-point filter bottleneck; and5. computing the irreducible components of   V   of a prescribed dimension.Computational evidence indicates that the efficiency of this numerical method willhave a significant impact on the structure of many of the algorithms of numericalalgebraic geometry, including the most fundamental computation: that of the nu-merical irreducible decomposition. For example, Section 3.3 shows that computingthe numerical irreducible decomposition of the system defined by taking the 2  ×  2adjacent minors of a 3 × 9 matrix of indeterminates [9, 14, 15] is well over an order of magnitude cheaper.Section 1 provides a brief overview of the basic definitions and concepts neededfor the remainder of the article. In particular, we present some background on nu-merical algebraic geometry [33] and we present the Dayton-Zeng approach [8, 39] tonumerically computing multiplicities.The algorithms themselves are presented in Section 2.1. The basic idea in calculusterms is that the dimensions  T  k  of the space of Taylor series expansions of degree atmost  k  of algebraic functions on  V   at the point  p  eventually grow like  O ( k dim p ( V    ) ).When dim p ( V   ) = 0, the dimensions  T  k  steadily grow until they reach the multiplicity µ  p  of the point  p  and are constant from that point on. If dim p ( V   )  >  0, thesedimensions steadily grow without bound. The number of paths  ν  p  ending at  p  forstandard homotopies used to compute  p  is an upper bound for the multiplicity of   µ p .Given such an upper bound  ν  p  for the multiplicity of   µ p , we have a simple test tocheck whether  p  is isolated:1. Compute the dimensions  T  k  until  k  =   k , where   k  := min { k  ≥  1  |  T  k  =  T  k − 1  or  T  k  > ν  p } . 2. Then  p  is isolated if and only if   T  b k  ≤  ν  p  if and only if   T  b k  =  T  b k − 1 .If   V   ⊂  C N  is  k -dimensional at a point  p , then, for 0  ≤  ℓ  ≤  k , a general linear space L  through  p  of dimension equal to  N   − k  +  ℓ  will meet  V   in an algebraic subset of dimension  ℓ  at  p . Using this fact, we turn the above algorithm for whether a point is 2  isolated into an algorithm for computing the maximum dimension of the componentsof   V   containing  p .At first sight, it seems strange to hypothesize, that for the given point  p  ∈  V   ,we have a positive integer, which, is (in the case that  p  is isolated) an upper boundfor the multiplicity of the system  F   at  p . In fact, such a number is typically thebyproduct of the algorithms that numerically compute  p .For example, assume that we are finding the isolated solutions of the system F  1 ,...,F  n  on C N  . If   n < N  , there are no isolated solutions, so we are in the situation n  ≥ N.If   n  =  N  , then many homotopies  H  ( x,t ) = 0 may be used to solve the system. Forexample, the classical homotopies starting from general multihomogeneous systemsfor  t  = 1, have the property for an isolated solution  p  of   F   that the number of pathsgoing to  p  as  t  →  0 equals the multiplicity of the system  F   at  p . This classical resultfollows from [32, Lemma A.1] and the paragraph following its proof. See also [26,  § 5].In the case that  n > N  , then the usual approach [33, Chapter 13.5] to findingisolated solutions of   F   is to first find isolated solutions of a randomized system G ( z ) :=  G 1 ( z 1 ,...,z N  )... G N  ( z )   :=   I  N   A  ·  F  1 ( z 1 ,...,z N  )... F  n ( z 1 ,...,z N  )   = 0where  I  N   is the  N   ×  N   identity matrix and  A  is a random (( n − N  ) × N  )-matrix.General theory tells us that each isolated multiplicity  µ  solution of   F   = 0 is an isolatedsolution of   G  of multiplicity at least  µ . The cascade homotopy of [26] has the aboveproperty.Section 1.2 describes the method of [8] and provides details on creating an efficientimplementation. Section 2.2 gives the local dimension algorithms. Examples arepresented in Section 3 to illustrate the new methods.Previous to this article the only theoretically rigorous numerical algorithm tocompute the local dimension of an algebraicset  V   at a point was the  global   algorithm of first computing the full numerical irreducible decomposition of Sommese, Verschelde,and Wampler, and then using one of their membership tests to determine whichcomponents of   V   the point is on.Though the local dimension test in this article is the first rigorous numerical localdimension algorithm that applies to arbitrary polynomial systems, it is not the first local   numerical method proposed to compute local dimensions. Using the facts aboutslicing  V   with linear spaces  L , if   V   ⊂ C N  is  k -dimensional at a point  p  then •  a general linear space  L  through  p , of dimension less than or equal to  N   − k ,will meet  V   in no other points in a neighborhood of   p ; and •  a general linear space  L  through  p , of dimension greater than  N   −  k , willmeet  V   in points in a neighborhood of   p ,Sommese and Wampler [31,  § 3.3] showed that the local dimension dim p ( V   ) couldbe determined by choosing an appropriate family  L t  of linear spaces with  L 0  =  L and then deciding whether the point  p  deforms in  V   ∩  L t . They did not presentany numerical method to make this decision. In [16], Kuo and Li present a usefulheuristical method to make this decision. The method works well for many examples,but it does not seem possible to turn it into a rigorous local-dimension algorithm fora point on the solution set of a polynomial system. For instance, since the method isbased on the presentation of the system and not on intrinsic properties of the solutionset, it is not likely that any result covering general systems can be proved. Indeed, as 3  the simple system in Section 3.2 (consisting of two cubic equations in two variables)shows: solution sets with multiple branches going through a point may well lead thatmethod to give false answers.More details regarding the specific algebra, geometry, and viewpoint utilized inthis article can be found in [5]. An in-depth description of the general algebraic andgeometric tools associated with this article can be found in [7, 10, 11, 13].Section A.2 provides the specific commutative algebra results which are used inthe development and implementation of the algorithms of Section 2. As is commonwith results about polynomial systems, we can work with either the given systemof polynomials or a system consisting of the homogenenizations of the polynomials.Though we work directly with the polynomials in the article proper, it is more naturalfor the theoretical discussion of the underlying commutative algebra in the appendix,to work with homogeneous polynomials and ideals. 1. Background material.  In this section we collect basic definitions and con-cepts from algebraic geometry and give a description of the Dayton-Zeng multiplicitymatrix sufficient for this article’s needs. 1.1. Background from numerical algebraic geometry.  A general refer-ence for the material presented here is [33]. The common zero locus of a set of multivariate polynomials is called an algebraic set. Let  F  1 ,F  2 ,...,F  n  be multivari-ate polynomials (with complex coefficients) in the variables  z 1 ,z 2 ,...,z N   and let V   =  V   ( F  1 ,F  2 ,...,F  n )  . =  {  p  ∈  C N  | F  i (  p ) = 0 for 1  ≤  i  ≤  n }  be the algebraic setdetermined by the system. The subset  V   ◦ of   V   consisting of manifold points is anopen subset of   V   that is dense in  V   in the usual topology on C N  . The algebraic set  V  is said to be  irreducible   if   V   ◦ is connected. An irreducible algebraic set is called a  va-riety  . For an arbitrary algebraic set  V   , there are finitely many connected components V   ◦ 1  ,...,V  ◦ r  of   V   ◦ . Setting  V  i  equal to the closure  V   ◦ i  , the decomposition V   =  V  1  ∪ ... ∪ V  r is called the irreducible decomposition of the algebraic set  V   . For this decomposition V  i  ⊂  j  = i V  j  for all  i . The dimension of   V  i  is defined to be the dimension of   V   ◦ i  , whichin turn is defined as the dimension of the complex tangent space of   V   ◦ i  at any point.The dimension of the algebraic set  V   is defined to be the largest dimension of thevarieties appearing in its irreducible decomposition. The dimension dim p ( V   ) of   V   ata point  p  ∈  V   is the maximum of the dimensions of the components containing  p .Numerically determining the decomposition of an algebraic set into varieties isa fundamental problem in numerical algebraic geometry and serves as crucial datafor other computations. Algorithms of Sommese, Verschelde, and Wampler that ac-complish this decomposition are presented in [27, 29, 30] and described with fullbackground details in [33]. The computation of the numerical irreducible decomposi-tion has been implemented in the numeric/symbolic systems Bertini [2] and PHCpack[36]. The algorithm is built around the well-established numerical method known ashomotopy continuation (other well known homotopy based software packages for find-ing isolated solutions of polynomial systems include HOM4PS–2.0 [17], Hompack [37],and PHoM [12]).If a variety,  V  i , has dimension  d  then its degree, deg( V  i ), is defined to be the num-ber of points in the intersection of   V  i  with a generic linear space of codimension  d . Agood reference for this is [25]. For an algebraic set,  V   , the basic algorithms of numeri-cal algebraic geometry produce discrete data in the form of a  witness point set   [27, 33]. 4  For each dimension  d , this consists of a set of points  W  d  and a generic codimension d  linear space  L d  with the basic property that within a user-specified tolerance, thepoints of   W  d  are the intersection of   L d  with the union of the  d -dimensional com-ponents of   V  . Since a general codimension  d  linear space meets each  d -dimensionalirreducible component,  V  i  of   V   , in exactly deg( V  i ) points, each  d -dimensional irre-ducible component has as many witness points in  W  d  as its degree. Let  D  denote thedimension of   V   =  V  ( F  1 ,F  2 ,...,F  n ). A  cascade algorithm   utilizing repeated applica-tions of homotopy continuation to polynomial systems constructed from  F  1 ,F  2 ,...,F  n yields the full witness set  W  0 ∪ W  1  ∪···∪ W  D . Each of the polynomial systems con-structed from  F   is obtained by adding extra linear equations (corresponding to slicesby generic hyperplane sections). Thus we also obtain equations for each of the linearspaces  L 0 ,L 1 ,...,L D  used to slice away the  W  i  from  V   and these linear spaces forma flag. In fact, a (possibly larger) witness superset ˆ W  i  containing  W  i  is obtained. Theextra  junk points   J  i  = ˆ W  i  \ W  i  actually lie on irreducible components of dimensiongreater than  i .  J  i  is separated from  W  i  using a so-called junk-point filter. Using theflag together with techniques such as monodromy, it is possible to partition  W  d  intosubsets, which are in one-to-one correspondence with the  d -dimensional irreduciblecomponents of   V  . In particular, the points in  W  d  can be organized into sets such thatall points of a set lie (numerically) on the same irreducible component.Thus, given a set of polynomials F  , it is possible to produce by numerical methodsa flag together with a collection of subsets of points such that the subsets are pairwisedisjoint and are in one-to-one correspondence with the irreducible components of the algebraic set determined by  F  . The points in a subset all lie within a prescribedtolerance of the irreducible component to which it corresponds. The number of pointsin the subset is the same as the degree of the irreducible component and the subsetis a numerical approximation of the intersection of the irreducible component witha known linear space of complementary dimension (coming from the flag). Classicalintroductions to continuation methods can be found in [1, 24]. For an overview of numerical algorithms and techniques for dealing with systems of polynomials, see[21, 33, 34]. For details on the cascade algorithm, see [26, 33].The polynomial system can be used to attach a positive integer, known as the multiplicity  , to each irreducible component of its corresponding algebraic set. Systemsof polynomial equations which impose a multiplicity greater than one on a compo-nent give rise to a special type of numerical instability which can require substantialcomputational effort to overcome, for example with the use of deflation [19, 20] andadaptive precision techniques [3]. These instabilities, in addition to bottlenecks thatarise in the decomposition and cascade algorithms, lead to a great slow-down in thecomputations involved in computing  W  d . A significant improvement in these algo-rithms will follow from the ability to efficiently resolve the following problem:  Given a point   p  which approximates a point lying on the algebraic set   V   =  V   ( F  1 ,F  2 ,...,F  n ) ,determine the dimension of the largest irreducible component of   V   which contains   p ,i.e., determine the local dimension dim p ( V   ) of   V   at  p . Indeed, the standard approachto the numerical irreducible decomposition processes components starting at the topdimensions and working down to zero-dimensional components, i.e., to isolated points.For systems for which only the isolated solutions are sought, this is computationallyexpensive. The local dimension test allows the processing for the numerical irreducibledecomposition to directly compute the decomposition of the set of components of aprescribed dimension  k  without first having to carry out the computation of witnesssets for all dimensions greater than  k . 5
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