Copositive Plus Matrices

Copositive Plus Matrices Willemieke van Vliet Master Thesis in Applied Mathematics October 2011

Copositive Plus Matrices Summary In this report we discuss the set of copositive plus matrices and their properties. We examine certain subsets of copositive plus matrices, copositive plus matrices with small dimensions, and the copositive plus cone and its dual. Furthermore, we consider the Copositive Plus Completion Problem, which is the problem of deciding whether a matrix with unspecified entries can be completed to obtain a copositive plus matrix. The set of copositive plus matrices is important for Lemke s algorithm, which is an algorithm for solving the Linear Complementarity Problem (LCP. The LCP is the problem of deciding whether a solution for a specific system of equations exists and finding such a solution. Lemke s algorithm always terminates in a finite number of steps, but for some problems Lemke s algorithm terminates with no solution while the problem does have a solution. However, when the data matrix of the LCP is copositive plus, Lemke s algorithm always gives a solution if such solution exists. Master Thesis in Applied Mathematics Author: Willemieke van Vliet First supervisor: Dr. Mirjam E. Dür Second supervisor: Prof. dr. Harry L. Trentelman Date: October 2011 Johann Bernoulli Institute of Mathematics and Computer Science P.O. Box 407 9700 AK Groningen The Netherlands

Contents 1 Introduction 1 1.1 Structure...................................... 1 1.2 Notation....................................... 1 2 Copositive Plus Matrices and their Properties 3 2.1 The Class of Copositive Matrices......................... 3 2.2 Properties of Copositive Matrices......................... 4 2.3 Properties of Copositive Plus Matrices...................... 5 2.4 Subsets....................................... 8 2.5 Small Dimensions.................................. 10 2.6 The Copositive Plus Cone and its Dual Cone.................. 13 2.6.1 The Copositive Plus Cone......................... 14 2.6.2 The Dual Copositive Plus Cone...................... 14 2.7 Copositive Plus of Order r............................. 15 2.8 Copositive Plus Matrices with 1, 0, 1 Entries.................. 15 3 The Copositive Plus Completion Problem 19 3.1 Unspecified Non-diagonal Elements........................ 19 3.2 Unspecified Diagonal Entries........................... 25 4 Lemke s Algorithm 29 4.1 The Linear Complementarity Problem...................... 29 4.2 Lemke s Algorithm................................. 31 4.3 Termination and Correctness........................... 35 4.3.1 Termination for Nondegenerate Problems................ 36 4.3.2 Termination for Degenerate Problems.................. 37 4.3.3 Conditions under which Lemke s Algorithm is Correct......... 40 4.4 Applications in Linear- and Quadratic Programming.............. 40 4.4.1 Linear Programming............................ 40 4.4.2 Quadratic Programming.......................... 43 4.5 Applications in the Game Theory......................... 44 4.5.1 Two Person Games............................. 45 4.5.2 Polymatrix Games............................. 46 4.6 An Application in Economics........................... 49 Nomenclature 53 iii

iv CONTENTS Index 55 Bibliography 57

Chapter 1 Introduction 1.1 Structure In 1968 Cottle and Dantzig proposed the Linear Complementarity Problem (LCP[2]. The LCP is the problem of deciding whether a solution for a specific system of equations exists. An algorithm for solving the LCP is Lemke s algorithm which is also called the complementary pivot algorithm. It was proposed by Lemke in 1965 [12] for finding equilibrium points. Lemke s algorithm always terminates in a finite number of steps, but for some problems Lemke s algorithm terminates with no solution while the problem does have a solution. However, when the data matrix of the LCP is Copositive Plus, Lemke s algorithm always gives a solution if such solution exists. In this report we discuss the LCP as well as Lemke s algorithm. Further, we examine the set of copositive plus matrices and their properties. In chapters 2 and 3, we focus on copositive plus matrices. In chapter 2, we discuss some basic properties of the copositive plus matrices. We examine certain subsets of copositive plus matrices, copositive plus matrices with small dimensions, and the copositive plus cone and its dual. Furthermore, we consider matrices which are copositive plus of order r and we consider copositive plus matrices with only 1, 0, 1 entries. In chapter 3, we discuss the Copositive Plus Completion Problem. We consider matrices in which some entries are specified and the remaining entries are unspecified and are free to be chosen, such matrices are called partial matrices. The choice of values for the unspecified entries is a completion of the partial matrix. The Copositive Plus Completion Problem is the problem of deciding which partial matrices have a copositive plus completion. In the first part of this chapter we examine matrices with unspecified non-diagonal entries and in the second part we examine matrices with unspecified diagonal entries. In chapter 4, we discuss the LCP and Lemke s algorithm. We show that Lemke s algorithm always terminates in a finite number of steps. Furthermore, we discuss some applications of the LCP: Linear and Quadratic programming, the problem of finding equilibrium points in two person and polymatrix games, and the problem of finding equilibrium points in economics. 1.2 Notation In this report we will use the following notation. The set of nonnegative matrices is denoted by N and the set of symmetric matrices is denoted by S. 1

2 CHAPTER 1. INTRODUCTION The set R is the set of real numbers. The set of nonnegative real numbers is denoted by R +. So if a vector v is in R n +, then all n entries of the vector v are nonnegative. Further, the n-dimensional sphere with radius 1 is defined as the set S n = {v R n+1 v = 1}. The nonnegative quadrant of this sphere is denoted by S n + = {v R n+1 + v = 1}. We denote the ith element of a vector v by v i and the element of the ith row and jth column of a matrix M is denoted by M ij. The vector e is the vector with ones everywhere. The unit vector e i is the vector with at the ith entry an one and zeros everywhere else. Inequality of vectors is always meant entry wise. For example, given a vector v, v 0 means that every entry of v is nonnegative. At last, the inner product of two vectors v 1 and v 2 is denoted by v 1, v 2 = v1 T v 2(= v2 T v 1. The norm of a vector v is given by v = v, v. Furthermore, the infinity norm of a vector v is given by v = max( v 1, v 2,..., v n.

Chapter 2 Copositive Plus Matrices and their Properties In the last sixty years, several articles about the properties of the set of copositive matrices are proposed; see for example [3], [4], [17], [16], [6] and [5]. Known is what the cone and the dual cone of these matrices look like and what we can say about this set of matrices for small dimensions. Further, many sufficient and necessary conditions are found for the copositive matrices. Much less is known about the copositive plus matrices, which form a subset of the copositive matrices. These matrices are introduced by C.E. Lemke [12] and the properties of these matrices have been studied by R.W. Cottle, G.J. Habetler, and C.E. Lemke in [3] and [4]; by A.J. Hoffman and F. Pereira in [8]; and by H. Väliaho in [17]. In this chapter the most important results of these articles will be presented and we will present some new theorems about copositive plus matrices. 2.1 The Class of Copositive Matrices We will give here the definitions of copositive and copositive plus matrices with respect to symmetric matrices. However, for every non symmetric matrix M, we have that M = 1 2 (M + M T is a symmetric matrix. So if a definition of a property holds for M, we say that the corresponding non symmetric matrix M also satisfies this property. We provide the following definitions and notation for the class of copositive matrices. Definition 1. Let M be a real symmetric n n matrix. The matrix M is said to be copositive, denoted by M C, if z T Mz 0 for all z 0. The matrix M is said to be copositive plus, denoted by M C +, if M C and for z 0, z T Mz = 0 implies Mz = 0. The matrix M is said to be strictly copositive if z T Mz > 0 for all nonzero z 0. The interior of C is the set of strictly copositive matrices. Therefore, if a matrix M is strictly copositive it will be denoted by M int(c. 3

4 CHAPTER 2. COPOSITIVE PLUS MATRICES AND THEIR PROPERTIES Note that for a non symmetric matrix M and its corresponding symmetric matrix M, the quadratic product z T Mz = 1 2 zt Mz + 1 2 zt M T z = z T 1 2 (M + M T z = z Mz. So above definitions almost holds for non symmetric matrices, the only difference is that for a non symmetric copositive plus matrix M, z 0 with z T Mz = 0 implies that (M +M T z = 0. A class of matrices which is close to the class of copositive matrices is the class of positive definite matrices. Definition 2. Let M be a real symmetric n n matrix. The matrix M is said to be positive semidefinite, denoted by M S +, if z T Mz 0 for all z. The matrix M is said to be positive definite, denoted by M S ++, if z T Mz > 0 for all z 0. Two important properties are the property of inheritance and the property of closure under principal rearrangements. All classes of matrices defined in this section satisfies both properties; see [3]. This first property is about the principal submatrices of a matrix, such a principal submatrix can be obtained by removing similarly indexed rows and columns of a given square matrix. The second property is about the principal rearrangements of a matrix, by a principal rearrangement of a matrix we mean a matrix P T MP where P is a permutation matrix. Definition 3. A class X satisfies the property of inheritance if any principal submatrix of a matrix in class X is again in class X. Further, a class X satisfies the property of closure under principal rearrangements if any principal rearrangement of a matrix in class X is again in class X. 2.2 Properties of Copositive Matrices Here we discuss some properties about the values of the entries of copositive matrices. It is easy to see that the diagonal elements of a copositive matrix must be nonnegative. This can be shown with proof by contradiction. Assume there is a copositive matrix M with M ii < 0, then a contradiction occurs for the quadratic product of M with the corresponding unit vector e i. The product e T i Me i = M ii < 0 and this contradicts with the copositivity of M. If all diagonal entries are equal to one, then we can say something about the other entries. This result is shown in the following theorem. Theorem 1. If M is a copositive n n matrix with M ii = 1 for all i, then ˆ the entries M ij 1 for all i j, ˆ the sum i j M ij n.

2.3. PROPERTIES OF COPOSITIVE PLUS MATRICES 5 Proof. We use in this proof that the quadratic product of a symmetric matrix M, with only ones on the diagonal, is equal to x T Mx = i,j M ij x i x j = i = i M ii x 2 i + i j M ij x i x j x 2 i + i j M ij x i x j. ˆ If x = e i + e j with i j, then the quadratic product x T Mx is equal to 2 + 2M ij. This product is nonnegative, since M is copositive and x 0. It follows that M ij 1 for all i j. ˆ If x = e, then x T Mx = n + i j M ij and again this is nonnegative. It follows that M ij n. i j This theorem requires that the diagonal entries are equal to one, however every matrix with positive diagonal entries can be scaled to a matrix with only ones on the diagonal. We can rewrite this theorem for general copositive matrices. Theorem 2. If M is a copositive matrix, then ˆ the entries M ij 1 2 (M ii + M jj for all i j, ˆ the sum i j M ij i M ii. Proof. This proof is similar to the proof of Theorem 1. The previous theorem gives a lower bound for the entry M ij for all i j. theorem gives a more tight lower bound for M ij. The next Theorem 3. If M is a copositive matrix, then M ij M ii M jj for all i j. Proof. If x = M jj e i + M ii e j with i j, then x T Mx = 2M ii M jj + 2M ij Mii M jj. This product is nonnegative, since M is copositive and x 0. It follows that M ij M ii M jj for all i j. 2.3 Properties of Copositive Plus Matrices A copositive plus matrix is copositive, so the results of the previous section hold for copositive plus matrices. In this section we discuss some specific results for copositive plus matrices. From the previous section we know that all the diagonal elements of a copositive plus matrix are nonnegative. If a copositive plus matrix has a zero diagonal entry, then this gives restrictions for the entries in the corresponding row and column.

6 CHAPTER 2. COPOSITIVE PLUS MATRICES AND THEIR PROPERTIES Theorem 4 ([3]. If M is copositive plus and M ii = 0, then M ij = M ji = 0 for all j. Note that Theorem 4 also holds for positive (semi definite matrices. With a principal rearrangement we can change the order of the rows and the columns in such a way that every zero column and the corresponding zero row moves respectively to the left and the bottom. This gives the following result. Theorem 5 ([3]. If M 0 is copositive plus, then there is a principal rearrangement M of M such that ( M A 0 =, 0 0 where A ii > 0. The following theorem gives another principal rearrangement for copositive plus matrices. Theorem 6 ([4]. Let M be a copositive n n matrix. M is copositive plus if and only if there is a principal rearrangement M of M which in block form is such that ( M A B = B T D, ˆ A is positive semidefinite r r matrix with 0 r n; ˆ B = AB, for some B ; ˆ D (B T AB is strictly copositive (hence D is strictly copositive. The following theorem, Theorem 7, is about strictly copositive matrices. Theorem 8 is a similar theorem about copositive plus matrices. Theorem 7. If M is a strictly copositive matrix, then there is an ɛ > 0 such that M ɛi is strictly copositive. Proof. Consider the constant k = Mx min x 0,x 0 (xt x 2. This k is well defined if this minimum exists. If x 0 and x 0, then there is a normalized vector y such that x = x y. We have that Mx min x 0,x 0 (xt x 2 = min y T My y 0, y =1 ( x 2 x 2 y 2 = min y 0, y =1 (yt My. (2.1 We take the minimum over the set S+ n 1 = {y Rn + y = 1}. This set is compact, because S n 1 + is a closed subset of S n 1 and S n 1 is compact. Furthermore, the function y y T My is continuous. The extreme value theorem states that the minimum (2.1 exists. So k is well defined.

2.3. PROPERTIES OF COPOSITIVE PLUS MATRICES 7 The matrix M is strictly copositive, so k is a positive constant. 0 < ɛ < k. If z 0 is an arbitrary vector with z 0, then Choose ɛ such that z T (M ɛiz = z T Mz ɛ z 2 > z 2 ( zt Mz z 2 k = z 2 ( zt Mz z 2 min x 0,x 0 (xt Mx x 2 0. Hence, if we choose ɛ such that 0 < ɛ < k, then the matrix M ɛi is strictly copositive. Theorem 8. Let M be a copositive plus matrix, let and let W = {i x ker(m R n + with x i > 0}, { 1 if i = j and i / W, (I W ij = 0 otherwise. There exists an ɛ > 0 such that the matrix M ɛi W is copositive plus. To prove this theorem we use the set Z = {y S n 1 + Here supp(x={i x i 0}. supp(x supp(y x ker(m Sn 1 + }. (2.2 Theorem 9. If M is a nonzero matrix, then the set Z, as defined by (2.2, is non-empty and compact. Proof. If M is a nonzero matrix, then there are indices i and j such that M ij 0. Therefore, the vector e j S n 1 + is not in the kernel of M. Further, the supp(e j = {j} and supp(x {j} for all x ker(m S n 1 +. So the vector e j Z and hence Z is non-empty. The set Z S n 1 + is bounded, since S+ n 1 is bounded. Left to show is that Z is closed. If y S n 1 + \ Z, then there is an x ker(m Sn 1 + such that supp(x supp(y. Let ɛ = min i supp(x y i > 0. We consider all w S n 1 + with w y < ɛ. Hence w S n 1 + w y < ɛ w y j < ɛ j w y j < ɛ j supp(x w y j < min i supp(x y i w j > 0 j supp(x supp(x supp(w. j supp(x \ Z. So for all y Sn 1 + \ Z, there is an ɛ > 0 such that all vectors w with \ Z. So the set Sn 1 + \ Z is open in Sn 1 + and therefor Z is closed. The set Sn 1 + is closed, so the set Z is closed. Hence Z is compact. w y < ɛ are in S n 1 + in S n 1 + We will now proof Theorem 8.

8 CHAPTER 2. COPOSITIVE PLUS MATRICES AND THEIR PROPERTIES Proof of Theorem 8. Consider the constant k = min x Z ( xt Mx x T I W x. This k is well defined and positive, since Z is nonempty, compact, and every x Z is not in the kernel of M. Choose ɛ such that 0 < ɛ < k. Take an arbitrary vector x 0 and x 0. There is a vector z such that x = x z and z = 1. The product x T (M ɛi W x 0 if and only if z T (M ɛi W z 0. We split the problem in three cases. ˆ Case 1: z ker(m. If z ker(m, then z ker(i W and z T (M ɛi W z = z T Mz ɛz T I W z = 0. ˆ Case 2: z Z. If z Z, then z T (M ɛi W z = z T Mz ɛz T I W z > z T Mz min x Z ( xt Mx x T I W x zt I W z z T Mz zt Mz z T I W z zt I W z = 0. ˆ Case 3: z / ker(m and z / Z. If z / Z, then there is an y ker(m with supp(y supp(z. If α = min (z i, i supp(y y i then z αy and there is an i supp(y such that z i = αy i. Let p = z αy 0. Due to the choice of α, supp(y supp(p. If p Z, then p T (M ɛi W p > 0; see case 2. If p / Z, then there is a v ker(m with supp(v supp(p and we can repeat previous steps until we find a p Z. We will eventually find a p Z, because due to the choice of α the supp of the remaining vector P becomes smaller. So there is a moment that there is no y ker(m with supp(y supp(p. Further, So z T Mz is positive. p T (M ɛi W p = (z αy T (M ɛi W (z αy = z T Mz. Hence, if we choose ɛ such that 0 < ɛ < k, then x T Mx 0 for all x 0. So M ɛi W is copositive. Furthermore, x 0 and x T Mx = 0 if and only if x ker(m. So M ɛi W is copositive plus. 2.4 Subsets In this section we discuss certain subsets of the copositive plus matrices. We have the following inclusions: int(c C + C and S ++ S +.

2.4. SUBSETS 9 These inclusions follow directly from the definitions of these sets. Two other inclusions which follow easily form the definitions are S ++ int(c and S + C. In the following theorem we see an inclusion which is not trivial. It is proved in [3], but here also another proof is proposed. Theorem 10 ([3]. Every positive semidefinite matrix is copositive plus, that is, S + C +. Proof. Let M be a positive semidefinite matrix. The matrix M is copositive, since S + C. Further, the matrix M has a Cholesky decomposition, that is, M = A T A. If z 0 and z T Mz = 0, then z T Mz = z T A T Az = (Az T Az = Az 2 = 0 Az = 0 A T Az = Mz = 0. So, if z 0 and z T Mz = 0, then Mz = 0 and hence M is copositive plus. The nonnegative matrices are a subset of the copositive matrices. However, it is not a subset of the copositive plus matrices. An example of a nonnegative matrix which is not copositive plus is the matrix ( 0 1 M =. 1 0 It follows directly from Theorem 4 that M is not copositive plus. However, there is a subset of the nonnegative matrices, for which every element is a copositive plus matrix. We define this subset as the flatly nonnegative matrices; see [4]. A matrix M is said to be flatly nonnegative, denoted by N +, if M N and M ii = 0 imply M ij = M ji = 0 for all i j. Note that the interior of the nonnegative matrices, the strictly positive matrices, is in N +. Theorem 11. Every flatly nonnegative matrix is copositive plus, that is, N + C +. Proof. Let M be a flatly nonnegative matrix. It is easy to see that M is copositive, because M N + N C. Left to prove is that x 0 with x T Mx = 0 implies Mx = 0. For x 0, every term of x T Mx = i,j x ix j M ij is nonnegative. So if x T Mx = 0, then all terms of the sum x T Mx = i,j x ix j M ij are zero. We have the following implications: x 0 and x T Mx = 0 x i x j M ij = 0 i, j So M is copositive plus. x 2 i M ii = 0 i x i = 0 M ii = 0 i x i = 0 M ii = M ij = M ji = 0 i, j (because M N + n (Mx j = M ij x i = 0 j Mx = 0 i=0

10 CHAPTER 2. COPOSITIVE PLUS MATRICES AND THEIR PROPERTIES N N + C + Figure 2.1: N C + = N + Further, if a copositive plus matrix is nonnegative it is also flatly nonnegative, this follows from Theorem 4. So N C + = N +. The Minkovski sum of two sets of matrices A and B is the result of adding every element of A to every element of B, that is, the set A + B = {a + b a A, b B}. We know that the Minkovski sum of the nonnegative matrices and the positive semidefinite matrices is a subset of the copositive matrices. We will show that the Minkovski sum of the flatly nonnegative matrices and the positive semidefinite matrices is a subset of the copositive plus matrices. Theorem 12. The Minkovski sum of the flatly nonnegative matrices and the positive semidefinite matrices is copositive plus, that is, N + + S + C +. Proof. Let A be a flatly nonnegative matrix and let B be a positive semidefinite matrix. The matrix A + B is copositive, because A + B N + + S + N + S + C. If x is a nonnegative vector, then x T (A + Bx = 0 x T Ax + x T Bx = 0 x T Ax = 0 and x T Bx = 0 (because A N + C, B S + C Ax = 0 and Bx = 0 (because A N + C +, B S + C +. So (A + Bx = 0 and hence A + B is copositive plus. 2.5 Small Dimensions In this section we discuss the properties of copositive plus matrices with small dimensions. We know already that for dimension n = 2, the set of copositive matrices is equal to N S +. So every copositive 2 2 matrix is either nonnegative and/or positive semidefinite. We can say something similar about copositive plus 2 2 matrices. Theorem 13. Let M be a symmetric 2 2 matrix. The matrix M is copositive plus if and only if it is flatly nonnegative or it is positive semidefinite. That is, C + 2 2 = N + 2 2 S+ 2 2. Proof. Let M be a copositive plus 2 2 matrix of the form ( a b M =. b c The matrix M is copositive, so a and c are nonnegative. We split the proof in two cases.

2.5. SMALL DIMENSIONS 11 ˆ Case 1: a, c > 0. If b 0, then M is flatly nonnegative, so we are done. If b < 0, we can easily prove that M is positive semidefinite. For x 0, we have x T Mx 0 because M is copositive. For x 0, we have x T Mx = ( x T M( x 0 because x 0 and M is copositive. Finally, if x has one positive entry and one negative entry, then x T Mx = ax 2 1 + 2bx 1x 2 + cx 2 2 has only positive terms and xt Mx 0. So x T Mx 0 for all x and hence M is positive semidefinite. ˆ Case 2: a and/or c is equal to zero. Without loss of generality we can say a = 0. From Theorem 4 it follows that b is zero as well, hence M is flatly nonnegative. So M is flatly nonnegative and/or positive semidefinite. So C 2 2 + N 2 2 + S+ 2 2, we already know that N + S + C + ; see Theorems 10 and 11. Hence, C 2 2 + = N 2 2 + S+ 2 2. Let the int(n be the strictly positive matrices. Note that N 2 2 + S+ 2 2 = int(n 2 2 S 2 2 +. So in Theorem 13 we can replace N 2 2 + S+ 2 2 with int(n 2 2 S 2 2 +. So a symmetric 2 2 matrix is copositive plus if and only if it is positive semidefinite or strictly positive. For n 3 the previous theorem does not hold. Consider the counterexample 1 8 10 1 M = 8 10 1 1. (2.3 1 1 1 If x 0, then x T Mx = (x 1, x 2, x 3 M(x 1, x 2, x 3 T = (x 1 x 2 2 + 2 5 x 1x 2 + 2x 1 x 3 + 2x 2 x 3. The product x T Mx > 0 for all x 0 and x 0, so M is strictly copositive and also copositive plus. However it is clearly not flatly nonnegative. Neither it is positive semidefinite, because for a vector x with x 1 = x 2 = 1 and x 3 = 1 the quadratic form of M is negative. Hannu Väliaho [17] has characterized all the copositive plus matrices of dimension n 3. Theorem 14 ([17]. Let M be a symmetric n n matrix with n 3. The matrix M is copositive plus if and only if it is positive semidefinite or, after deleting the possible zero rows and columns, strictly copositive. This theorem is proved in [17]. In this proof is used that a copositive plus 3 3 matrix of the form 1 a b M = a 1 c, b c 1 with a, b, c 1 and a < 1, b < 1 or c < 1 is positive semidefinite. However this is not always true, see for a counterexample matrix (2.3. Therefore, we propose a different and more detailed proof here. For this proof we need the following theorem for copositive matrices. Theorem 15 ([6]. Let M be a symmetric 3 3 matrix. The matrix M is copositive if and only if the conditions M 11 0, M 22 0, M 33 0, (2.4 M 12 M 11 M 22, M 23 M 22 M 33, M 13 M 11 M 33, (2.5

12 CHAPTER 2. COPOSITIVE PLUS MATRICES AND THEIR PROPERTIES are satisfied, as well as at least one of the following conditions: M 12 M33 + M 23 M11 + M 31 M22 + M 11 M 22 M 33 0, (2.6 det(m 0. (2.7 The matrix is strictly copositive if and only if these conditions are satisfied with strict inequality in (2.4, (2.5 and (2.7. We will now proof Theorem 14. Proof of Theorem 14. Sufficiency is immediate, because both S + and int(c are subsets of C +. Further, necessity is clear for n = 1. The necessity for n = 2 follows from Theorem 13, because if a matrix is flatly nonnegative, then it is also, after deleting the possible zero rows and columns, strictly copositive. So it is left to show that this theorem holds for n = 3. If a 3 3 matrix has zero rows and columns, we can delete them and we obtain a matrix of lower dimension. For matrices with dimension lower than three we have already proved that the theorem is correct. So for n = 3 it suffices to consider the scaled matrix M = 1 a b a 1 c b c 1 The matrix M is copositive, so it satisfies (2.4 and (2.5 and at least one of (2.6 or (2.7; see Theorem 15. The diagonal entries are one, so condition (2.4 is strict. From (2.5 it follows a, b, c 1. If a, b, c 0, then M is strictly copositive. Let us now consider the cases with at least one of a, b, c is negative, assume without loss of generality a < 0. We split the proof in three cases: ˆ Case 1: (2.5 is not strict, take a = 1. If x = e 1 + e 2, then x T Mx = 0. The vector Mx = 0, since M is copositive plus. In particular, (Mx 3 = bx 1 + cx 2 + x 3 = (b + c = 0 and therefore b = c. We know that a, b, c 1, so b and c are less or equal one. One of the eigenvalues of M is equal to zero and the other eigenvalues are equal to λ = 3 2 ± 1 2 1 + 8b 2. The value of b 2 is between zero and one, so these two eigenvalues are nonnegative. This gives that all eigenvalues are nonnegative, so M is positive semidefinite. ˆ Case 2: (2.5 is strict and (2.6 is satisfied or (2.7 with strict inequality sign is satisfied. It follows from Theorem 15 that M is strictly copositive. ˆ Case 3: (2.5 is strict, det(m= 0, and (2.6 is not satisfied. One of the eigenvalues of M is zero, since det(m= 0. The other eigenvalues are equal to λ = 3 2 ± 1 2 3 + 4(a 2 + b 2 + c 2, note that the eigenvalues are real because the matrix M is symmetric. Further, the values of b, c, a < 1, since (2.5 is strict and (2.6 is not satisfied.therefore, a 2 +b 2 +c 2 < 3 and all eigenvalues are nonnegative, so M is positive semidefinite. So we have proved that M is positive semidefinite or strictly copositive plus..

2.6. THE COPOSITIVE PLUS CONE AND ITS DUAL CONE 13 In [17] is given an example which shows that the preceding theorem does not hold for dimensions larger than n = 3. Consider the copositive plus matrix ( M11 M M = 12 = M 21 M 22 1 1 0 0 1 1 0 0 0 0 1 2 0 0 2 1 Here M 11 is positive semidefinite but not strictly copositive, and M 22 is strictly copositive but not positive semidefinite. In the following theorem we characterize again the 2 2 and 3 3 copositive plus matrices, but this time the characterization also holds for 4 4 copositive plus matrices. The following theorem looks like the theorem for copositive n n matrices with n 4, which say that C n n = N n n + S n n + for n 4; see [13]. Theorem 16. Let M be a symmetric n n matrix with n 4. The matrix M is copositive plus if and only if there is a flatly nonnegative matrix A and a positive semidefinite matrix B such that A + B = M. That is, C n n + = N n n + + S+ n n with n 4. Proof. We know N + + S + C + ; see Theorem 12. Left to show is that for n 4 holds that C + N + + S +. Let M be a symmetric n n matrix with n 4, let and let W = {i x ker(m R n + with x i > 0}, { 1 if i = j and i / W, (I W ij = 0 otherwise. From Theorem 8 it follows that there is an ɛ > 0 such that the matrix M ɛi W is copositive. Therefore there exists an A N and a B S + such that M ɛi W = A + B; see [13]. It follows that M = A + ɛi W + B := à + B with à = A + ɛi W N. If x ker(m R n +, then. x T Mx = 0 x T Ãx + x T Bx = 0 x T Ãx = 0 and x T Bx = 0 x T Ãx = 0 and Bx = 0, (2.8 x T Mx = 0 Mx = 0 Ãx + Bx = 0. (2.9 The implications (2.8 and (2.9 imply that every x ker(m R n + is in the kernel of Ã. This together with à N gives that if i W or j W, then Ãij = 0. Furthermore, we have that à ii ɛ > 0 for all i / W. Therefor, à is flatly nonnegative. We have construct a matrix à N + and a matrix B S + such that M = à + B. Therefore, C+ N + + S +. 2.6 The Copositive Plus Cone and its Dual Cone The set of copositive matrices is a closed convex pointed cone with nonempty interior. In this section we will see that the set of copositive plus matrices is also a cone and that it shares many properties with the copositive cone. However, the copositive plus cone is not closed and we examine its closure. At the end of this section we will consider the dual cone of the copositive plus matrices.

14 CHAPTER 2. COPOSITIVE PLUS MATRICES AND THEIR PROPERTIES 2.6.1 The Copositive Plus Cone A set K is called a cone if for every x K and every α 0, we have αx K. Furthermore, a set K is called a convex cone if for every x, y K and every λ 1, λ 2 0, we have λ 1 x+λ 2 y K. A cone K is pointed if K K = {0}, that is, the cone K does not contain a straight line. In the next theorem we will see that the set of copositive plus matrices is a convex cone. Theorem 17. The set of copositive plus matrices is a convex pointed cone with nonempty interior. Proof. Take two arbitrary copositive plus matrices A and B and scalars λ 1, λ 2 > 0. Let D be the convex combination of the matrices A and B, that is, D = λ 1 A + λ 2 B. The matrix D is copositive, because the set of copositive matrices is a convex cone. The matrices A and B are both copositive, so z T Az 0 and z T Bz 0 for all z 0. If z 0 and z T Dz = 0, then z T Dz = λ 1 z T Az + λ 2 z T Bz = 0 z T Az = 0 and z T Bz = 0 Az = 0 and Bz = 0. Hence, Dz = λ 1 Az + λ 2 Bz = 0. Consequently, D is copositive plus and the set of copositive plus matrices is a convex cone. The copositive cone is pointed and the copositive plus cone is a subset of this cone, so the copositive plus cone is also pointed. Further, the interior of C, the set of strictly copositive matrices, is a subset of C + and the interior of C is non empty. Hence, the interior of C + is nonempty. The copositive plus cone is not closed. We will illustrate this with an example in dimension n = 2. Consider the sequence of 2 2 matrices of the form ( ai 1 M = 1 a i where a i is a sequence of positive numbers which converges to zero. Each matrix in this sequence is copositive plus, because they are all in N +. However the matrix where the sequence converges to, is not copositive plus. Hence, the copositive plus cone is not closed. Theorem 18. The closure of the copositive plus matrices is the set of copositive matrices. Proof. We have that cl(int(c cl(c + cl(c, since int(c C + C. The closure of C is C, since C is closed. Furthermore, the closure of the interior of C is C. Hence, the closure of C + is C., 2.6.2 The Dual Copositive Plus Cone The definition of the dual cone of a set K is equal to K = {A S A, B 0 for all B K}, where A, B = trace(a T B. The dual cone of the copositive matrices is equal to C = {A S n n A = F F T with F N n m }; see [6]. Theorem 19. The dual cone of the copositive plus matrices is equal to the dual cone of the copositive matrices. That is, (C + = {A S n n A = F F T with F N n m }.

2.7. COPOSITIVE PLUS OF ORDER R 15 Proof. The dual cone of the copositive matrices C (C +, since C + C. Left to show is that (C + C. We will prove that if M / C, then M / (C +. If matrix M / C, then there is a matrix B C with B, M < 0. For every ɛ > 0, the matrix B + ɛi int(c C + and for ɛ small enough B + ɛi, M = B, M + ɛi, M < 0. So if M / C, then M / (C +. Hence, (C + C. 2.7 Copositive Plus of Order r Matrices which are not copositive plus, can still have copositive plus principal submatrices. In this section we will consider matrices for which all r r principal submatrices are copositive plus. More precise, we say that M is copositive plus of order r if and only if every r r principal submatrix is copositive plus. We will present here some theorems with necessary conditions, but also sufficient conditions for a matrix to be copositive plus. Theorem 20 ([16]. If M R n n is copositive plus of order n 1 but not strictly copositive, then it is copositive plus if and only if it is singular. Theorem 21 ([17]. If M R n n has p < n positive eigenvalues, then it is copositive plus if and only if it is copositive plus of order p + 1. Theorem 22 ([17]. If M R n n is of rank r < n, then it is copositive plus if and only if it is copositive plus of order r. 2.8 Copositive Plus Matrices with 1, 0, 1 Entries In this section we characterize the matrices with 1, 0, 1 entries. Let E be the set of symmetric matrices with ones on the diagonal and zeros, ones and minus ones elsewhere. In [8], A. J. Hoffman and F. Pereira have shown under which conditions a matrix in E is copositive, copositive plus or positive semidefinite. Below, we will give part of their main results. For this, we will refer to the following set of 3 3 matrices: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 (2.10 (2.13 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 (2.11 (2.14 1 1 1 1 1 0 1 0 1 (2.12 Theorem 23 ([8]. Let A E. ˆ The matrix A is copositive if and only if it has no 3 3 principal submatrices which, after principal rearrangement, are of the form (2.10 or (2.11. ˆ The matrix A is positive semidefinite if and only if it has no 3 3 principal submatrices which, after principal rearrangement, are of the form (2.10-(2.14.

16 CHAPTER 2. COPOSITIVE PLUS MATRICES AND THEIR PROPERTIES ˆ The matrix A is copositive plus if and only if A contains no 3 3 principal submatrices which, after principal rearrangement, are of the form (2.10, (2.11, (2.13, (2.14. Let G 1 (A, G 0 (A and G 1 (A be undirected graphs associated with a symmetric n n matrix A E. Here we define G 1 (A(G 0 (A, G 1 (A to be the graph with n vertices such that the vertices i and j are adjacent if and only if A ij = 1(A ij = 0, A ij = 1. The characterization of the graphs G 1 (A, G 0 (A and G 1 (A, where A is a copositive matrix, is given in [8]. The following theorem is about the graphs G 1 (A, G 0 (A and G 1 (A, where A is copositive plus. Theorem 24. Let A E. The matrix A C + if and only if each of the following statements is true: 1. G 1 (A contains no triangles. 2. G 1 (A contains those edges (i, j where i and j are at distance 2 in G 1 (A. 3. G 0 (A contains those edges (i, j where i and j are at distance 2 in G 1 (A. 4. G 1 (A G 0 (A, G 1 (A G 1 (A, or G 0 (A G 1 (A contains a triangle for every subset of three vertices. Proof. Statement 1 excludes submatrix (2.10, statement 2 excludes submatrix (2.11, statement 3 excludes submatrix (2.14 and statement 4 excludes submatrix (2.13. A subset of E are the symmetric matrices with ones on the diagonal and ones and minus ones elsewhere. This set is denoted by E +. Rewriting Theorem 23 for matrices in E + gives the following theorem. Theorem 25. Let A E +. ˆ The matrix A is copositive if and only if it has no 3 3 principal submatrices which, after principal rearrangement, are of the form (2.10. ˆ The matrix A is positive semidefinite if and only if it has no 3 3 principal submatrices which, after principal rearrangement, are of the form (2.10, (2.14. ˆ The matrix A is copositive plus if and only if A contains no 3 3 principal submatrices which, after principal rearrangement, are of the form (2.10, (2.14. Proof. Delete in Theorem 23 all principal submatrices which contain zero s and we are left with Theorem 25. Given a matrix A E +, Theorem 25 gives the same conditions for A to be copositive plus or to be positive semidefinite. We get the following theorem. Theorem 26. Let M E +. The matrix M is copositive plus if and only if M is positive semidefinite. Let G(M be an undirected graph associated with a symmetric n n matrix M E +. Here we define G(M to be the graph with n vertices such that the vertices i and j are adjacent if and only if M ij = 1. The following theorem is about the graphs G(M, where M is copositive plus.

2.8. COPOSITIVE PLUS MATRICES WITH 1, 0, 1 ENTRIES 17 Theorem 27 ([7]. Let M E +. The matrix M is copositive plus (or positive semidefinite if and only if G(M is K p,n p for some 0 < p < n. Here K p,n p is a complete bipartite graph with partitions of size p and size n p.

18 CHAPTER 2. COPOSITIVE PLUS MATRICES AND THEIR PROPERTIES

Chapter 3 The Copositive Plus Completion Problem In this chapter we consider matrices in which some entries are specified, but where the remaining entries are unspecified and are free to be chosen. Such matrices are called partial matrices. A choice of values for the unspecified entries is a completion of the partial matrix. In a completion problem we ask for which partial matrices we can find a completion such that some desired property is satisfied. The (strictly copositive (plus completion problem is the problem of deciding which partial matrices have a (strictly copositive (plus completion. A necessary condition for a partial matrix to have a (strictly copositive (plus completion is that all fully specified principal submatrices have the desired property, otherwise the property of inheritance is violated; see Definition 3. A partial matrix for which every fully specified principal submatrix is (strictly copositive (plus is partial (strictly copositive (plus. 3.1 Unspecified Non-diagonal Elements Throughout this section, we assume that all diagonal entries of a partial matrix are specified. We first assume that only one pair of non-diagonal entries is unspecified, without loss of generality we can take the entries in the upper right and lower left corners as the unspecified entries. So in this section, we consider the partial matrix A of the form A = a b T? b A c? c T d, (3.1 where the question marks denote the unspecified entries. For (strictly copositive matrices it is shown that every partial (strictly copositive matrix has (strictly copositive completions. See the following theorem. Theorem 28 ([10]. If A is a partial copositive matrix of the form (3.1, then a b T s A = b A c s c T d is a copositive matrix for s ad. If A is partial strictly copositive, then A with s ad is strictly copositive. 19

20 CHAPTER 3. THE COPOSITIVE PLUS COMPLETION PROBLEM We cannot replace copositive in Theorem 28 with copositive plus. Consider the counterexample 1 1 s A = 1 1 2. (3.2 s 2 1 This matrix is partial copositive plus, because the upper left 2 2 submatrix is positive semidefinite and the lower right 2 2 submatrix is flatly nonnegative; see Theorems 10 and 11. If we take x = (1, 1, 0 T, then x T Ax is zero. But Ax = (0, 0, s + 2 T and this cannot be zero if s ad = 1. In this section we will see that this matrix is an example of a partial copositive plus matrix which does not have a copositive plus completion at all. The following theorem tells us under which conditions a partial copositive plus matrix has a copositive plus completion. Theorem 29. If A is a partial copositive plus matrix of the form (3.1, then a b T s A = b A c (3.3 s c T d is a copositive plus matrix if s ad and the following two conditions hold for x 0: ( a b T b A x = 0 ( s c T x = 0, (3.4 ( A c c T x = 0 ( b d T s x = 0. (3.5 To prove this theorem, we need the following theorem. Theorem 30. Let A be a copositive matrix and let x be a strictly positive vector. If x T Ax = 0, then Ax = 0. Proof. Consider the model min x 0 xt Ax. The objective value of this model is always nonnegative, because A is copositive. Further, if x = 0, then x T Ax = 0. So the absolute minimum value of this model is zero. We need to show that if x is an absolute minimum, then Ax = 0. For proving this we will use the KKT-conditions. Let f(x = x T Ax and let g i (x = x i 0 for i = 1,..., n. The KKT-conditions for this model are n n f(x + µ i g i (x = 2Ax µ i e i = 0, i=1 µ i 0, µ i g i (x = µ i x i = 0, and g i (x = x i 0 for all i. A vector x satisfies the constraint qualifications if it satisfies the linear independence constraint qualification, that is, the gradients of the active inequality constraints are linearly independent at x. Further, if x is a local minimum that satisfies the constraint qualifications, then there exists constants µ i such that the KKT-conditions hold. i=1

3.1. UNSPECIFIED NON-DIAGONAL ELEMENTS 21 Take a vector x > 0 with x T Ax = 0, then x is an absolute minimum of the model. All inequality constraints of the model are not active, g i (x > 0, since x > 0. Consequently, the linear independence constraint qualification is satisfied and therefore there exist constants µ i such that the KKT-conditions hold. From µ i g i(x = 0 it follows that µ i = 0 for all i. The first KKT-condition becomes f(x = 0. The gradient f(x = 2Ax = 0, so Ax = 0. Before proving Theorem 29, we introduce some notation. Let A be a matrix of the form (3.3, then we denote the principal submatrix of the first n 1 columns by A u and the principal submatrix of the last n 1 columns by A l, that is, ( ( a b T A c A u = b A and A l = c T. d Recall that if A is partial copositive plus, every fully specified principal submatrix is copositive plus. Therefore, A u and A l are copositive plus. Further, let x R n be the vector which consists of the three components x 1, x n R and x R n 2 such that x T = (x 1, x T, x n. Let x T u = (x 1, x T and let x T l = (x T, x n. The quadratic product of A is equal to x T Ax = ax 2 1 + x T A x + dx 2 n + 2x 1 b T x + 2x n c T x + 2sx 1 x n, (3.6 Further, the product Ax is equal to = x T u A u x u + dx 2 n + 2x n c T x + 2sx 1 x n, (3.7 = x T l A lx l + ax 2 1 + 2x 1 b T x + 2sx 1 x n. (3.8 Ax = ax 1 + b T x + sx n bx 1 + A x + cx n sx 1 + c T x + dx n. (3.9 Proof of Theorem 29. The first restriction for s, s ad, guarantees the copositivity of the matrix A; see Theorem 28. Left to show is that matrix A is copositive plus. Take an x 0 such that x T Ax = 0, we will show that this always implies Ax = 0. We split the problem in five cases, the case when x > 0, the three cases where respectively x 1 = 0, x n = 0 and x = 0 and the final case where x 1, x n, x 0 and x i = 0 for certain i. ˆ Case 1: x > 0. The vector Ax = 0, since A is copositive and x > 0; see Theorem 30. ˆ Case 2: x 1 = 0. Consider the terms of Ax like in (3.9. The first term of each entry of Ax is zero, since x 1 = 0. Further, A l x l = 0, since x T Ax = x T l A lx l = 0 and A l is copositive plus. If A l x l = 0, then b T x + sx n = 0; see restriction (3.5. So the sum of the last two terms of each entry of Ax is also zero. Hence, Ax = 0. ˆ Case 3: x n = 0. This case can be proven analogously to case 2, but in this case we need restriction (3.4. ˆ Case 4: x = 0. Consider the terms of Ax like in (3.9. The second term for each entry of Ax is zero, since x = 0. The product x T Ax = ax 2 1 + dx2 n + 2sx 1 x 2 = 0 and all terms of x T Ax are nonnegative. Therefore, all terms of x T Ax are zero. If ax 2 1 = 0, then x 1 = 0 or a = 0. If a = 0, then b = 0; see Theorem 4. Furthermore, if a and b are zero and we substitute x = e 1 in restriction (3.4, then also s = 0. So

22 CHAPTER 3. THE COPOSITIVE PLUS COMPLETION PROBLEM a = b = s = 0 or x 1 = 0, which shows that the first term of each entry of Ax is zero. If dx n = 0, then we can show in a similar way that the last term of each entry of Ax is zero. Hence, all terms of each entry of Ax are zero, so Ax = 0. ˆ Case 5: x 1, x n, x 0 and there is an i such that x i = 0. Let X = {x = (x 1, x T, x n T R n + x 1, x n, x 0, i s.t. x i = 0, x T Ax = 0}. We will show with an iterative process that if x X, then Ax = 0. For this, we introduce the set X left and at the start of the process X left = X. We will show for one vector x at the time that Ax = 0. If we have shown for a vector x X left that Ax = 0, then we remove all vectors βx with β 0 from X left. We will continue this process until X left is empty and then we have proved that Ax = 0 for all vectors x of X. For all vectors x 0, x / X left, and x T Ax = 0 we have already proven that Ax = 0 in a previous case or in a previous step of this case. So if a vector x 0 and x T Ax = 0 is not in X left, then Ax = 0. If X left is not empty, then we take a vector x X left for which there is no vector w X left with supp(w supp(x, where supp(x = {i x i 0}. For all i with x i = 0, we delete the ith row and ith column of A. The remaining matrix à has at least dimension 3, since x 1 0, x n 0, and x has at least one nonzero element. Consider the matrix à = a b T s b à c s c T d Further, let Ãu denote the principal submatrix of à which consists of all the rows and columns of à except from the last row and column. Let Ãl denote the principal submatrix of à which consists of all the rows and columns of à except from the first row and column. Finally, x is the vector which we get if we remove all zeros from x. Like wise, if we go back from x to x we add zeros to x. Note that à is not partial strictly copositive. This can be shown with contradiction, if à is partial strictly copositive, then à is strictly copositive since s ad. This is not true, because there exists a x 0 with x T à x = 0. So à is not partial strictly copositive and therefore, one of the matrices Ãu or Ãl is not strictly copositive. Assume without loss of generality that Ãu is not strictly copositive. So there is an vector p T 0 and p 0 such that p T à u p = 0. If α = min( x i p i, then α p = (αp 1, α p (x 1, x and there is an i such that α p i = x i. Consider the vector x 1 x x n = αp 1 α p 0. ( v1 ṽ + Due to the choice of α, the vector ṽ has at least one zero. Therefore, supp(ṽ supp( x and also supp(v supp(x. Remember that we have chosen x in such a way that there is no vector w X left with supp(w supp(x. So v in not in X left. v n.

3.1. UNSPECIFIED NON-DIAGONAL ELEMENTS 23 We can rewrite the product à x as follows, ( α p à x = à 0 + Ãṽ. (3.10 The matrix à is copositive, because it is a principal submatrix of A. Further, the vector x is strictly positive. The product x T à x = x T Ax = 0, from Theorem 30 it follows that à x = 0. Further, p T à u p = 0 p T A u p = 0 A u p = 0 (A u C + ( p A = 0 (see(3.4 (3.11 0 ( p à = 0. 0 So Ãṽ = 0, since à x = 0 and Ã(α pt, 0 T = 0; see (3.10. We have that Ãṽ = 0 ṽ T Ãṽ = 0 v T Av = 0 Av = 0 (v / X left. (3.12 Finally, ( αp Ax = A 0 + Av = 0, since A(αp T, 0 T = 0 and Av = 0; see (3.11 and (3.12. Remove all vectors βx with β 0 from X left and repeat this process until X left is empty. The restrictions (3.4 and (3.5 are necessary. For example, if (3.4 is not satisfied, then there is a vector x u such that A u x u = 0 and (s, c T x u 0. The vector x = (x T u, 0 T gives x T Ax = x T u A u x u = 0, but (Ax n = (s, c T x u 0. So (3.4 is necessary. It can be shown analogously that (3.5 is necessary. So matrices which cannot satisfy both (3.4 and (3.5 do not have a copositive plus completion. An example of this is matrix A = 1 1 1 s 1 1 1 0.5 1 1 1 0 s 0.5 0 1 This matrix is partial copositive plus, because all principal submatrices are positive semidefinite. Take x 1 = (1100 T and x 2 = (1010 T, then x T 1 Ax 1 and x T 2 Ax 2 are both zero. Further, Ax 1 is zero if and only if s 0.5 = 0 and Ax 2 is zero if and only if s = 0. So we cannot find a value for s for which (3.4 is satisfied. So this matrix does not have a copositive plus completion..

24 CHAPTER 3. THE COPOSITIVE PLUS COMPLETION PROBLEM In [10] it is mentioned that the restriction s ad is not always necessary, there are examples for which we can choose s smaller. For examples we refer to [10]. However, the restriction s ad is necessary for copositivity; see Theorem 3. If a partial copositive plus matrix A, then S A is the set of all possible values for s such that (3.4 and (3.5 are satisfied. We can have the following cases: ˆ There is a value s S A such that s ad The matrix A has a copositive plus completion. (see Theorem 29 ˆ The set S A is empty or all s S A satisfy s ad The matrix A does not have a copositive plus completion. (see Theorem 3 ˆ All s S A satisfy s ad and there is at least one s S A such that s ad It is possible that the matrix A has a copositive plus completion. An example of the second case is matrix (3.2. The following matrix is an example of the third case: 1 1 s A = 1 1 1 s 1 1 This matrix is partial copositive plus, because the upper left 2 2 submatrix is positive semidefinite and the lower right 2 2 submatrix is positive. From (3.4 and (3.5 it follows that s = 1. If s = 1, then the matrix A is copositive plus; see Theorem 25. So this is an example of a partial copositive plus matrix, for which we cannot see with the theorem that it has a copositive plus completion. Another example of the third case is the following matrix: A = 1 1 1 s 1 1 1 1 1 1 1.1 0 s 1 0 1 This matrix is partial copositive plus, because the upper left 3 3 submatrix is positive semidefinite and the lower right 3 3 submatrix is flatly nonnegative. From restrictions (3.4 and (3.5 it follows that s must be -1. If s = 1, then A is not copositive, since the quadratic product of A and the vector e e 2 is 9 10. So this is an example, which show that we cannot replace the restriction s ad with s ad. Theorem 31. If A is a partial copositive plus matrix of the form (3.1, then ˆ The matrices which has an s which satisfies all conditions of Theorem 29 have certainly a copositive completion. ˆ Further, the matrices which has no s ad which satisfies both restrictions (3.4 and (3.5 cannot be completed to a copositive plus matrix. For the other partial copositive plus matrices we cannot say whether the matrix has a copositive plus completion.