A Monotone Semismooth Newton Method for a Kind of Tensor Complementarity Problem ()
1. Introduction
A Tensor is a multi-arrray. We denote by
the space of all m-order and n-dimension tensors.
is in the form of
If the entries
are invariant under any permutation of their indices, then
is called a symmetric tensor. We denote by
the space of all symmetric tensors in
.
In this paper, we discuss the following tensor complementarity problem (simplified as TCP
): finding a point
such that
(1.1)
where
,
and
.
is an n-dimensional vector, whose ith component is given by [1]
is an
matrix, whose ith row and jth column is given by
It is obvious that
.
Tensor complementarity problem has many applications, such as nonlinear compressed sensing, communications, DNA microarrays, n-person noncooperative game and so on, see for example [2] [3]. TCP has received much attention and has taken good progress in recent years [4] - [13], such as the structure of the solution set, the global uniqueness solvability and the error bound and so on. Bai, Huang and Wang [4] proved that the P-tensor complementarity problem has a nonempty compact solution set. What’s more, they showed the global uniqueness solvability property for the TCP with a strong P-tensor. Che, Qi and Wei [5] showed that when the tensor is a positive definite or strictly copositive tensor, the TCP has a nonempty compact solution set. On the other hand, however, the study in the related numerical methods is very few. Luo, Qi and Xiu [3] proposed an iterative method to find the sparsest solutions to theZ-tensor complementarity problem with a non-positive constant term. Xu, Li and Xie [14] concerned with the tensor complementarity problem with a positive semi-definite Z-tensor. Under the assumption that the problem has a solution at which the strict complementarity holds, they showed that the problem is equivalent to a system of lower dimensional tensor equations. In this paper, we present a semismooth Newton method for TCP, under the assumption that
is a concave function, we establish the monotone convergence theorem for the proposed method. Here
is concave means
is a concave function,
.
For convenience of presentation, we introduce some concepts and notations which will be used throughout the paper. We denote
. Let
with
. Denote by
the r-dimensional subvector of
and its elements are
,
. Similarly, we denote
as the r-dimensional subvector of q and its elements are
,
.
2. Semismooth Newton Method and Its Convergence
We first introduce a new class of structure tensor, called M-like tensor.
Definition 2.1. A tensor
is called an M-like tensor, if for
,
is an M-matrix.
Remark 2.1. As an example, it is easy to verify that the even-order diagonal tensors with all positive diagonal entries are M-like tensors. We give a non-diagonal M-like tensor.
Example 2.1. Let
be a 4th-order 2-dimensional tensor with elements
It is easy to verify
is an M-like tensor over
. In the following, without specification, we always suppose
is an M-like tensor.
Let
be the well-known Fischer-Burmeister function defined by
(2.1)
It is easy to see that TCP (1.1) can be transformed to the system of nonsmooth equations:
(2.2)
where
.
Let
denote the B-subdifferential of H at
.
, if it satisfies
For any
can be expressed as follows.
(2.3)
where
(2.4)
It is easy to see that
and
for
.
Now, we present semismooth Newton method for TCP (1.1).
Algorithm 2.1 (Semismooth Newton Method)
Step 0. Choose
and initial point
. Let
.
Step 1. If
, stop.
Step 2. Choose
, calculate
satisfying the following linear equations
(2.5)
Step 3. Calculate
(2.6)
Let
and goto Step 1.
Theorem 2.2. [15] Let
be the solution of (1). Then the sequence
generated by (6) is Q-quadratically convergent to
if
is sufficiently close to
.
In the following, we will prove the sequence
monotonically converges to the solution of (1). First, we give some useful lemmas.
Lemma 2.3. [16] Let
be an M-matrix and
satisfy
for
. Then B is an M-matrix and
Furthermore, any principle submatrix of A is again an M-matrix.
By the definition of V, the ith row
of V can be presented by
where
is the ith row of
and
is the ith row of the
identity matrix. It is easy to verify
, where
with
and
with
where
is the ith diagonal element of
. Noting that
with the off-diagonal element being nonpositive, we have B is an M-matrix and
by Lemma 2.3. This together with K being a positive diagonal matrix, we obtain the following lemma.
Lemma 2.4. Let the matrix V be defined by (2.3), then V is an M-matrix.
Define the set D by
As p always belongs to D, D is not empty.
Lemma 2.5. Let
be the solution of (1.1). Then
and
Proof. Since
is the solution of (1.1), we have
and
. This implies that
.
For any
, we have
and
. Associated to x, we define two disjoint index sets as follows:
This together with the fact
implies
for all
. Since
, we have
Noting that
is an M-like tensor, by Theorem 4 in [17], we have
is a strong T-monotone function. Hence by Lemma 2.2 in [18], we immediately have that
. This completes the proof.
Lemma 2.6. Let
. Then the sequence
generated by (2.6) is contained in D and satisfies
Proof. We prove the conclusion by induction. For simplicity, we denote
and
by x and y respectively. Suppose
, we only need to verify
and
. Since
, by (2.5) we have
Hence, we get
(2.7)
Now, we prove
. Associated to x, we define four disjoint index sets as follows:
Then by (2.1), (2.2), (2.4), we have
(2.8)
If
, by (2.3), (2.5), (2.8) we get
and
. This together with (2.2), (2.1) implies
.
If
,
. Since
is concave, by (2.3), (2.5), (2.8) again, we have
Hence,
.
If
, by (2.5) and (2.3), we obtain
where
and
. Hence, we have
If
, then
, which implies
.
If
, then by (2.4) we have
, and then
. Since
is an M-like tensor,
and
for
, we have
Hence,
.
Similarly, if
, by simple calculations,
and hence
Therefore, we have
Hence,
The above argument has shown
. From (2.7), we have
and
. The proof is completed.
Noting that the sequence
is uniformly bounded, by Lemmas 2.5 and 2.6 and Theorem 2.2, it is easy to obtain the following theorem.
Theorem 2.7. Let
and
be the sequence generated by (2.6). Then we have for all
Moreover, the sequence
converges to
Q-quadratically.
3. Conclusion
We have introduced a new kind of structure tensor and discussed the numerical algorithm for TCP. By transforming the TCP to the system of nonsmooth equations, we have presented a semismooth Newton method for TCP. At each iteration, only linear equations need to be solved. The sequence generated by the algorithm is monotonically convergent to the solution of the TCP under proper conditions. This method can be regarded as a kind of Newton-iteration method. There are still some interesting future works that need to be done. For example, we can extend Algorithm 2.1 for other structure TCP and discuss its convergence.
Founding
The work was supported by the Educational Commission of Guangdong Province, China (grant No. 2019KTSCX172).