### 最佳化 Optimization 地 點 ： M 3 1 0 數 學 館

TMS Annual Meeting

### 數 學 年 會

## 2018 ^{數 學 年 會}

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### 演講摘要

Speech Abstracts

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### Strong Convexity of Sandwiched Entropies and Related Optimization Problems

### Yongdo Lim

### Department of Mathematics Sungkyunkwan University

### E-mail: ylim@skku.edu

We present several theorems on strict and strong convexity for sandwiched
quasi-relative entropy (a parametrised version of the classical fidelity). These
are crucial for establishing global linear convergence of the gradient projection
algorithm for optimization problems for these functions. The case of the
classi-cal fidelity is of special interest for the multimarginal optimal transport problem
*(the n-coupling problem) for Gaussian measures. This is joint work with *
Ra-jendra Bhatia and Tanvi Jain.

### 77

### KKM theorems in Hadamard manifolds

### Shue-Chin Huang

### Department of Applied Mathematics National Dong Hwa University E-mail: shuang@gms.ndhu.edu.tw

The purpose of this talk is to present a fixed point theorem for generalized KKM mappings in the Hadamard manifold settings. We derive the finite inter-section property of this class of mappings. As an application of this property, we also discuss the existence conditions on the generalized equilibrium prob-lem. This research is supported by a grant MOST 106-2115-M-259-005 from the Ministry of Science and Technology of Taiwan.

### 78

### Network data envelopment analysis with common weights

### Cheng-Feng Hu

### Department of Applied Mathematics National Chiayi University E-mail: cfhu@mail.ncyu.edu.tw

Common weight models can combat the computational burden of data en-velopment analysis (DEA) in the big data environment. This work considers studying a common-weights general network DEA model which is applicable to most network systems, except those with feedbacks and cycles. It shows that the general network DEA model with common weights can be reduced into an auxiliary fuzzy bi-objective mathematical programming problem by applying the basic principle of compromise of TOPSIS. The case of Taiwanese non-life insurance companies is utilized for illustration and comparison purposes. Our results show that the proposed common-weights network DEA model not only compares DMUs on a common base, but also produces reliable results in mea-suring efficiencies.

### 79

### New generalizations of Ekeland’s variational principle and well-known fixed point theorems

### with applications to nonconvex optimization problems

### Wei-Shih Du

### Department of Mathematics National Kaohsiung Normal University

### Email: wsdu@mail.nknu.edu.tw

In this talk, we establish new generalizations of Ekeland’s variational princi-ple, Caristi’s fixed point theorem, Takahashi’s nonconvex minimization theorem and nonconvex maximal element theorem for uniformly below sequentially lower semicontinuous from above functions and essential distances. New simultane-ous generalizations of fixed point theorems of Mizoguchi-Takahashi type, Nadler type, Banach type, Kannan type, Chatterjea type and others are also presented.

As applications, we concentrate on studying nonconvex optimization and mini-max theorems in metric spaces.

**Keywords:** Nonconvex optimization, minimax theorem, Ekeland’s
varia-tional principle, Caristi’s (common) fixed point theorem, Takahashi’s
noncon-vex* minimization theorem, nonconvex maximal element theorem, MT -function *
(or* R-function), MT (λ)-function, uniformly below sequentially lower *
semicon-tinuous from above, essential distance, Mizoguchi-Takahashi’s fixed point
theo-rem, Nadler’s fixed point theorem, Banach contraction principle, Kannan’s fixed
point theorem, Chatterjea’s fixed point theorem.

### 80

### Deep Learning for Region of Interest Based Clustering of White Matter Fibers

### Feng-Sheng Tsai

### Department of Biomedical Imaging and Radiological Science China Medical University

### E-mail: fstsai@mail.cmu.edu.tw

To cluster white matter fibers in whole-brain tractography, anatomical re-gions of interest (ROIs) are selected manually in brain diffusion MRI. Those ROIs are used to isolate tracts and cluster fiber bundles accordingly. Deep learning approaches may be applied to voxel-based ROI segmentation imme-diately; however, the number of voxels in ROIs is extremely smaller than the number of voxels in whole brain images, so they always suffer from the class im-balance problem when extracting related voxels of ROIs for training. Here we propose a hierarchical sampling technique to resolve the class imbalance problem of deep learning. ROI segmentation with deep learning is divided into hierar-chical sub-tasks, from 2-dimensional objective-plane explorations to restricted, bounded hot-zone locations, and then to voxel-based discrimination. Sampling datasets in all sub-tasks are more balanced for training. Specifically, two ROIs for clustering arcuate fasciculus in whole-brain tractography are presented.

### 81

### A block symmetric Gauss-Seidel decomposition theorem and its applications in big data

### nonsmooth optimization

### De-Feng Sun

### Department of Applied Mathematics The Hong Kong Polytechnic University

### E-mail: defeng.sun@polyu.edu.hk

The Gauss-Seidel method is a classical iterative method of solving the linear
system* Ax = b. It has long been known to be convergent when A is *
symmet-ric positive definite. I n t his t alk, w e s hall f ocus o n i ntroducing a symmetric
version of the Gauss-Seidel method and its elegant extensions in solving big
data nonsmooth optimization problems. For a symmetric positive semidefinite
linear* system Ax = b with x = (x*1*, . . . , x**s*) being partitioned into s blocks, we
show that each cycle of the block symmetric Gauss-Seidel (block sGS) method
exactly solves the associated quadratic programming (QP) problem but added
with an extra proximal term. By leveraging on such a connection to
optimiza-tion, one can extend the classical convergent result, named as the block sGS
decomposition theorem, to solve a convex composite QP (CCQP) with an
addi-tional* nonsmooth term in x*1. Consequently, one is able to use the sGS method
to solve a CCQP. In addition, the extended block sGS method has the
flexi-bility of allowing for inexact computation in each step of the block sGS cycle.

At the same time, one can also accelerate the inexact block sGS method to
achieve* an iteration complexity of O(1/k*^{2}) after performing k block sGS cycles.

As a fundamental building block, the block sGS decomposition theorem has played a key role in various recently developed algorithms such as the proxi-mal ALM/ADMM for linearly constrained multi-block convex composite conic programming (CCCP) and the accelerated block coordinate descent method for multi-block CCCP.

### 82

### Strong duality in minimizing a quadratic form subject to two homogeneous quadratic

### inequalities over the unit sphere

### Ruey-Lin Sheu

### Department of Mathematics National ChengKung University E-mail: rsheu@mail.ncku.edu.tw

This problem, called (P), is a contrast with a simpler version (P* ^{′}*) which also
minimizes a quadratic form but has just one homogeneous quadratic constraint
over the unit sphere. The inclusion of an additional homogeneous quadratic
constraint can cause (P) to have a positive duality gap, although the simpler
version (P

*) has been proved to adopt strong duality under Slater’s condition.*

^{′}On the surface the underlined problem (P) appears to be different f rom the CDT (Celis-Dennis-Tapia) problem. Their SDP relaxations, however, share a very similar format. The minute observation turns out to be valuable in deriving a necessary and sufficient condition for (P) to admit strong du ality. We will see that, in the sense of strong duality results, problem (P) is a generalization of the CDT problem. Many nontrivial examples are constructed in the paper to help understand the mechanism. Finally, as the strong duality in quadratic optimization is closely related to the S-lemma, we derive a new extension of the S-Lemma with three homogeneous quadratic inequalities over the unit sphere, with and without the Slater condition.

**Keywords:** Quadratically constrained quadratic programming, CDT
prob-lem, S-lemma, Slater condition, Joint numerical range

### 83

### Phase retrieval algorithms with random masks

### Peng-Wen Chen

### Department of Applied Mathematics National Chung Hsing University

### E-mail: pengwen@nchu.edu.tw

Phase retrieval aims to recover one unknown vector from its magnitude measurements, e.g., coherent diffractive imaging, where phase information is missing. The recovery of phase information can be formulated as one minimiza-tion problem subject to a non convex high-dimensional torus set. In theory, uniqueness of solutions can be obtained under random masks. The introduction of random masks actually breaks the symmetry of Fourier matrices and cre-ates spectral gap for the local convergence of many phase retrieval algorithms, including alternative projection methods and Fourier Douglas-Rachford algo-rithms. The spectral gap is related to the local convergence rate.

On the other hand, these alternative algorithms still could fail to generate the global solution effectively. To alleviate the stagnation of possible local solutions, we propose one null vector method as an initialization method for phase retrieval algorithms. The method is motivated by the following observation: Gaussian random vectors in high dimensional space are always nearly orthogonal to each other. According to magnitude data, we can construct one sub-matrix assem-bled from the sensing vectors nearly orthogonal to the unknown vector. One candidate for the initialization vector is given by the singular vector of the sub-matrix corresponding to the least singular value. Thanks to isometric Fourier matrices, this vector coincides with the dominant singular vector of the com-plement sub-matrix. Empirical studies (non-ptychography and ptychography) indicate that its incredible closeness to the unknown vector, compared with other existing methods. In this talk, we present one nonasymptotic error bound in the case of random complex Gaussian matrices, which sheds some light on its superior performance in the Fourier coherent diffractive case with random masks.

**Keywords: random masks, phase retrieval, null vector method.**

### 84

### The solvabilities of SOCEiCP and SOCQEiCP

### Wei-Ming Hsu

### Department of Mathematics National Taiwan Normal University

In this paper, we study the solvabilities of two optimization problems asso-ciated with second-order cone, including eigenvalue complementarity problem associated with second order cone (SOCEiCP), and quadratic eigenvalue com-plementarity problem associated with second order cone (SOCQEiCP). First of all, we try to rewrite the SOCEiCP as instances of the SOCCP. Secondly, we also try to rewrite SOCQEiCP as instances of SOCCP. Furthermore, we study some algorithms for solving SOCEiCP and SOCQEiCP.

**Keywords: Solvability, eigenvalue, second-order cone.**

**References**

*[1] A. Seeger, Eigenvalue analysis of equilibrium processes defined by linear*
*complementarity conditions, Linear Algebra and its Applications, vol. 292,*
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*[2] A. Seeger, Quadratic eigenvalue problems under conic constraints, SIAM*
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*[3] M. Queiroz, J. Júdice,C. Humes, The symmetric eigenvalue *
*comple-mentarity problem, Mathmatics of Computation, vol. 73, no.248 ,pp. *
1849-1863, 2003.

*[4] S. Adly, H. Rammal, A new method for solving second-order cone *
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*[5] C. Brás, M. Fukushima, A. Iusem, J. Júdice, On the quadratic *
*eigen-value complementarity problem over a general convex cone, Applied *
Math-ematics and Computation, vol. 271, pp. 391-403, 2015.

*[6] C. Brás, A. Iusem, J. Júdice, On the quadratic eigenvalue *
*complemen-tarity problem, Journal of Global Optimization, vol. 66, issue 2, pp. 153-171,*
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### 85

*[7] L. Fernandes, M. Fukushima, J. Júdice, H. Sherali, The *
*second-order cone eigenvalue complementarity problem, Optimization Methods and*
Software, vol. 31, issue 1, pp. 24-52, 2016.

*[8] J. Tao, M. Gowda, Some P-Properties for Nonlinear Transformations on*
*Euclidean Jordan Algebras, Mathematical Methods of Operations Research,*
vol. 30, no. 4, pp. 985-1004, 2005.

*[9] S.-H. Pan, S. Kum, Y. Lim, J.-S. Chen, On the generalized *
*Fischer-Burmeister merit function for the second-order cone complementarity *
*prob-lem, Mathematics of Computation, vol. 83, no. 287, 1143-1171, 2014.*

*[10] J. Wu, J.-S. Chen, A proximal point algorithm for the monotone *
*second-order cone complementarity problem, Computational Optimization and *
Ap-plications, vol. 51, no. 3, pp. 1037-1063, 2012.

*[11] J.-S. Chen, S.-H. Pan, A survey on SOC complementarity functions and*
*solution methods for SOCPs and SOCCPs, Pacific Journal of Optimization,*
vol. 8, no. 1, pp. 33-74, 2012.

*[12] S.-H. Pan, J.-S. Chen, A least-square semismooth Newton method for the*
*second-order cone complementarity problem, Optimization Methods and*
Software, vol. 26, no. 1, pp. 1-22, 2011.

*[13] S.-H. Pan, J.-S. Chen, A semismooth Newton method for SOCCPs based*
*on a one-parametric class of complementarity functions, Computational*
Optimization and Applications, vol. 45, no. 1, pp. 59-88, 2010.

*[14] S.-H. Pan, J.-S. Chen, A linearly convergent derivative-free descent*
*method for the second-order cone complementarity problem, Optimization,*
vol. 59, no. 8, pp. 1173-1197, 2010.

*[15] J.-S. Chen, S.-H. Pan, A one-parametric class of merit functions for the*
*second-order cone complementarity problem, Computational Optimization*
and Applications, vol. 45, no. 3, pp. 581-606, 2010.

*[16] S.-H. Pan, J.-S. Chen, A damped Gauss-Newton method for the *
*second-order cone complementarity problem, Applied Mathematics and *
Optimiza-tion, vol. 59, no. 3, pp. 293-318, 2009.

*[17] S.-H. Pan, J.-S. Chen, A regularization method for the second-order cone*
*complementarity problems with the Cartesian P*0*-property, Nonlinear *
Anal-ysis: Theory, Methods and Applications, vol. 70, no. 4, pp. 1475-1491,
2009.

*[18] J.-S. Chen, S.-H. Pan, A descent method for solving reformulation of the*
*second-order cone complementarity problem, Journal of Computational and*
Applied Mathematics, vol. 213, no. 2, pp. 547-558, 2008.

### 86

*[19] J.-S. Chen, Conditions for error bounds and bounded level sets of some*
*merit functions for SOCCP, Journal of Optimization Theory and *
Applica-tions, vol. 135, no. 3, pp. 459-473, 2007.

*[20] J.-S. Chen, Two classes of merit functions for the second-order cone *
*com-plementarity problem, Mathematical Methods of Operations Research, vol.*

64, no. 3, pp. 495-519, 2006.

*[21] J.-S. Chen, A new merit function and its related properties for the *
*second-order cone complementarity problem, Pacific Journal of Optimization, vol.*

2, no. 1, pp. 167-179, 2006.

*[22] J.-S. Chen, P. Tseng, An unconstrained smooth minimization *
*reformu-lation of second-order cone complementarity problem, Mathematical *
Pro-gramming, vol. 104, no. 2-3, pp. 293-327, 2005.

### 87

### Penalty and barrier methods for second-order cone programming

### Nguyen Thanh Chieu Department of Mathematics National Taiwan Normal University

### E-mail: thanhchieu90@gmail.com

In this talk we will present penalty and barrier methods for solving convex second-order cone programming:

min *f (x)*

s.t *Ax− b ≼*_{K}* ^{n}*0

*where A is an n× m matrix with n ≥ m, rank A = m. f : ℜ*^{m}*→ (−∞, +∞] is*
a closed proper convex function. *K** ^{n}* is a second-order cone (SOC for short) in

*ℜ*

*given by*

^{n}*K** ^{n}*:={

*(x*1*, x*2)*∈ ℜ × ℜ*^{n}^{−1}*| ∥x*2*∥ ≤ x*1

}*,*

where*∥ · ∥ denotes the Euclidean norm.*

This class of methods is an extension of penalty and barrier methods for convex optimization which was presented by A. Auslender et al. in 1997. With this method, we provide under implementable stopping rule that the sequence generated by the proposed algorithm is bounded and that every accumulation point is a solution to the considered problem. Furthermore, we examine effec-tiveness of the algorithm by means of numerical experiments.

**Keywords: Second-order cone, penalty and barrier methods, asymptotic**
functions, recession functions, convex analysis, smoothing functions.

### 88

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*J. Optimization, 10, pp. 211-230, 1999.*

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*matrices and over the Lorentz cone, Optimization methods and software,*
pp. 359-376, 2003.

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*opti-mization and variational inequalities, Springer monographs in *
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*con-vex semidefinite programming, Mathematics of Operations Research, 63,*
pp.195-219, 2006.

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*Theory and Algorithms, 3rd Edition, Wiley - Interscience, 2006.*

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*prove convexity of some soc-functions, Journal of Nonlinear and Convex*
*Analysis, Vol 13, No. 3, pp. 421-431, 2012.*

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12, pp. 436-460, 2002.

### 89

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*[15] N. Parikh and S. Boyd, Proximal Algorithms, Foundations and Trends in*
*Optimization, Vol. 1, No. 3, pp. 123-231, 2013.*

[16] S. H. Pan and J. S. Chen, A proximal-like algorithnm using quasi
*D-function for convex second-order cone programming, J. Optim. Theory*
*Appl., 138 , pp. 95-113, 2008.*

[17] S. H Pan and J. S. Chen, A class of interior proximal-like algorithms for
*convex second-order cone programming, SIAM J. Optim. Vol. 19, No. 2,*
pp. 883-910, 2008.

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*Applications. Vol. 49, pp. 61-99, 2011.*

### 90

### Neural networks based on three classes of NCP-functions for solving nonlinear

### complementarity problems

### Jan Harold M. Alcantara Department of Mathematics National Taiwan Normal University

### E-mail: janharold27@yahoo.com

We consider a family of neural networks for solving nonlinear
complementar-ity problems (NCP). The neural networks are based from the merit functions
induced by three classes of NCP-functions: the generalized natural residual
function and its two symmetrizations. We first provide a characterization of the
stationary points of the induced merit functions. To describe the level sets of the
merit functions, we prove some important properties related to the growth
be-havior of the complementarity functions. Furthermore, we analyze the stability
of the steepest descent-based neural network model for NCP. To illustrate the
theoretical results, we provide numerical simulations using our neural network
and compare it with other similar neural networks in the literature which are
based on other well-known NCP-functions. The numerical results suggest that
*the neural network has a better performance when their common parameter p*
is smaller. We also found that one among the three families of neural networks
we considered is capable of outperforming other existing neural networks.

This is a joint work with Jein-Shan Chen.

**Keywords: NCP-function, Neural network, natural residual function, **
sta-bility.

**References**

*[1] Y.-L. Chang, J.-S. Chen, C.-Y. Yang, Symmetrization of generalized*
*natural residual function for NCP, Operations Research Letters, 43(2015),*
354-358.

*[2] J.-S. Chen, C.-H. Ko, and S.-H. Pan, A neural network based on *
*gen-eralized Fischer-Burmeister function for nonlinear complementarity *
*prob-lems, Information Sciences, 180(2010), 697-711.*

### 91

*[3] J.-S. Chen, C.-H. Ko, and X.-R. Wu, What is the generalization of *
*nat-ural residual function for NCP, Pacific Journal of Optimization, 12(2016),*
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### 92

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### 93

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### 94

### 統計 Statistics 地 點 ： M 2 1 1 數 學 館

TMS Annual Meeting

### 數 學 年 會

## 2018 ^{數 學 年 會}

### D e c . 8 / 0 9 : 3 0 - 2 1 : 0 0

### D e c . 9 / 0 9 : 3 0 - 1 5 : 5 0

### 演講摘要

Speech Abstracts

ѱ䗜փ ঊ䗜փ

### D e c . 8 / 0 9 : 3 0 - 2 1 : 0 0

1 1 : 2 0 - 1 2 : 0 5

### တᙗឬ

<L&KLQJ<DR

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1 3 : 3 0 - 1 4 : 1 5

### ⧁㏣㧷

:HL&KLQJ:DQJ 䂋զཝᆮуੂޛᆮ㇗䚉ҁ㎧䀾⁗ශ

1 4 : 2 0 - 1 4 : 4 5

### 䲩ᱛ

&KXQ6KX&KHQ 0HDVXULQJVWDELOL]DWLRQLQPRGHOVHOHFWLRQ

### 哹ь䊠

6KLK+DR+XDQJ

2SWLPDOGHVLJQVIRUELQDU\UHVSRQVHPRGHOVZLWKPXOWLSOHQRQQHJDWLYH

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