PRIMAL PROGRAM LINEAR MENGGUNAKAN ALGORITMA INTERIOR POINT DAN METODE SIMPLEX

ABSTRACTPrimal linear programing in of section by operation reseach, “The obyective of Interior Point algoritm and Simplex Methods to make Solution in primal linear programing",  simplex method could be formulated to be interior point algoritm linear programing with MatLab basic program to get optimal solution of linear programing obyektif function. Analysis of interior point algoritm by Narendra Karmarkar (1984) from AT. ET. Bell are used interior point choice in feasible region, alpha criteria and primal-dual of linear programing. This study interior point algoritm in primal linear programing solution also makes to simplex method by George Dantzig (1947) solution in linear programing. Interior point algoritm in primal linear programing solution the fastest five hundred of  Simplex method, this news the best of  The New York Times1.Keywords :   Interior Point,  Alpha Criteria,  Simplex , Primal linear programing

Closing Duality Gaps of SDPs through Perturbation

Let $({\bf P},{\bf D})$ be a primal-dual pair of SDPs with a nonzero finite duality gap. Under such circumstances, ${\bf P}$ and ${\bf D}$ are weakly feasible and if we perturb the problem data to recover strong feasibility, the (common) optimal value function $v$ as a function of the perturbation is not well-defined at zero (unperturbed data) since there are ``two different optimal values'' $v({\bf P})$ and $v({\bf D})$, where $v({\bf P})$ and $v({\bf D})$ are the optimal values of ${\bf P}$ and ${\bf D}$ respectively. Thus, continuity of $v$ is lost at zero though $v$ is continuous elsewhere. Nevertheless, we show that a limiting version ${v_a}$ of $v$ is a well-defined monotone decreasing continuous bijective function connecting $v({\bf P})$ and $v({\bf D})$ with domain $[0, \pi/2]$ under the assumption that both ${\bf P}$ and ${\bf D}$ have singularity degree one. The domain $[0,\pi/2]$ corresponds to directions of perturbation defined in a certain manner. Thus, ${v_a}$ ``completely fills'' the nonzero duality gap under a mild regularity condition. Our result is tight in that there exists an instance with singularity degree two for which ${v_a}$ is not continuous.Comment: 26 pages. Comments welcom

A Superlinearly Convergent Primal-Dual Infeasible-Interior-Point Algorithm for Semidefinite Programming

. A primal-dual infeasible-interior-point path-following algorithm is proposed for solving semidefinite programming (SDP) problems. If the problem has a solution, then the algorithm is globally convergent. If the starting point is feasible or close to being feasible, the algorithms finds an optimal solution in at most O( p nL) iterations, where n is the size of the problem and L is the logarithm of the ratio of the initial error and the tolerance. If the starting point is large enough then the algorithm terminates in at most O(nL) steps either by finding a solution or by determining that the primal-dual problem has no solution of norm less than a given number. Moreover, we propose a sufficient condition for the superlinear convergence of the algorithm. In addition, we give two special cases of SDP for which the algorithm is quadratically convergent. Key words. semidefinite programming, path-following, infeasible-interior-point algorithm, polynomiality, superlinear convergence. AMS ..

Convergence and polynomiality of primal-dual interior-point algorithms for linear programming with selective addition of inequalities

This paper presents the convergence proof and complexity analysis of an interior-point framework that solves linear programming problems by dynamically selecting and adding relevant inequalities. First, we formulate a new primal–dual interior-point algorithm for solving linear programmes in non-standard form with equality and inequality constraints. The algorithm uses a primal–dual path-following predictor–corrector short-step interior-point method that starts with a reduced problem without any inequalities and selectively adds a given inequality only if it becomes active on the way to optimality. Second, we prove convergence of this algorithm to an optimal solution at which all inequalities are satisfied regardless of whether they have been added by the algorithm or not. We thus provide a theoretical foundation for similar schemes already used in practice. We also establish conditions under which the complexity of such algorithm is polynomial in the problem dimension and address remaining limitations without these conditions for possible further research

Quantum Interior Point Methods for Semidefinite Optimization

We present two quantum interior point methods for semidefinite optimization problems, building on recent advances in quantum linear system algorithms. The first scheme, more similar to a classical solution algorithm, computes an inexact search direction and is not guaranteed to explore only feasible points; the second scheme uses a nullspace representation of the Newton linear system to ensure feasibility even with inexact search directions. The second is a novel scheme that might seem impractical in the classical world, but it is well-suited for a hybrid quantum-classical setting. We show that both schemes converge to an optimal solution of the semidefinite optimization problem under standard assumptions. By comparing the theoretical performance of classical and quantum interior point methods with respect to various input parameters, we show that our second scheme obtains a speedup over classical algorithms in terms of the dimension of the problem $n$, but has worse dependence on other numerical parameters

리만 최적화와 그래프 신경망에 기반한 저 랭크 행렬완성 알고리듬에 관한 연구

학위논문(박사)--서울대학교 대학원 :공과대학 전기·정보공학부,2020. 2. 심병효.최근, 일부의 관측치로부터 행렬의 모든 원소들을 복원하는 방법으로 저 랭크 행렬 완성 (LRMC)이 많은 주목을 받고 있다. LRMC는 추천 시스템, 위상 복원, 사물 인터넷 지역화, 영상 잡음 제거, 밀리미터 웨이브 통 신등을 포함한 다양한 응용분야에서 사용되고 있다. 본 논문에서는 LRMC에 대해 연구하여 LRMC의 가능성과 한계에 대한 더 나은 이해를 할 수 있도록 기존 결과들을 구조적이고 접근 가능한 방식으로 분류한다. 구체적으로, 최신 LRMC 기법들을 두 가지 범주로 분류한 다음 각각 의범주를 분석한다. 특히, 행렬의 고유한 성질과 같은 LRMC 기법을 사용 할때 고려해야 할 사항들을 분석한다. 기존의 LRMC 기법은 가우시안 랜 덤행렬과 같은 일반적인 상황에서 성공적이었으나 많은 실제 상황에서 는복원하고자 하는 저 랭크 행렬이 그래프 구조 또는 다양체 구조와 같은 비유클리드 구조를 가질 수 있다. 본 논문에서는 실제 응용에서 LRMC의 성능을 향상시키기 위해 이 런추가적인 구조가 활용될 수 있음을 보인다. 특히, 사물 인터넷 네트워 크지역화를 위한 유클리드 거리 행렬 완성 알고리듬을 제안한다. 유클리 드거리 행렬을 낮은 랭크를 갖는 양의 준정부호 행렬의 함수로 표현한다. 이러한 양의 준정부호 행렬들의 집합은 미분이 잘 정의되어 있는 리 만다양체를 형성하므로 유클리드 공간에서의 알고리듬을 적당히 변형하 여LRMC에 사용할 수 있다. LRMC를 위해 우리는 켤레 기울기를 활용 한리만 다양체에서의 지역화 (LRM-CG)라 불리는 변경된 켤레 기울기 기 반알고리듬을 제안한다. 제안하는 LRM-CG 알고리듬은 관측된 쌍 거리 가특이값에 의해 오염되는 시나리오로 쉽게 확장 될 수 있음을 보인다. 실제로 특이값을 희소 행렬로 모델링 한 다음 특이값 행렬을 규제 항으 로LRMC에 추가함으로써 특이값을 효과적으로 제어 할 수 있다. 분석을 통 해LRM-CG 알고리듬이 확장된 Wolfe 조건 아래 원래 유클리드 거리 행렬 에선형적으로 수렴하는 것을 보인다. 모의 실험을 통해 LRM-CG와 확 장버전이 유클리드 거리 행렬을 복구하는 데 효과적임을 보인다. 또한, 그래프 모델을 사용하여 표현될 수 있는 저 랭크 행렬 복원을 위 한그래프 신경망 (GNN) 기반 기법을 제안한다. 그래프 신경망 기반의 LRM C(GNN-LRMC)라 불리는 기법은 복원하고자 하는 행렬의 그래프 영 역특징들을 추출하기 위해 변형된 합성곱 연산을 사용한다. 이렇게 추출 된특징들을 GNN의 학습 과정에 활용하여 행렬의 원소들을 복원할 수 있다. 합성 및 실제 데이터를 사용한 모의 실험을 통하여 제안하는 GNN -LRMC의 우수한 복구 성능을 보였다.In recent years, low-rank matrix completion (LRMC) has received much attention as a paradigm to recover the unknown entries of a matrix from partial observations. It has a wide range of applications in many areas, including recommendation system, phase retrieval, IoT localization, image denoising, milimeter wave (mmWave) communication, to name just a few. In this dissertation, we present a comprehensive overview of low-rank matrix completion. In order to have better view, insight, and understanding of potentials and limitations of LRMC, we present early scattered results in a structured and accessible way. To be specific, we classify the state-of-the-art LRMC techniques into two main categories and then explain each category in detail. We further discuss issues to be considered, including intrinsic properties required for the matrix recovery, when one would like to use LRMC techniques. However, conventional LRMC techniques have been most successful on a general setting of the low-rank matrix, say, Gaussian random matrix. In many practical situations, the desired low rank matrix might have an underlying non-Euclidean structure, such as graph or manifold structure. In our work, we show that such additional data structures can be exploited to improve the recovery performance of LRMC in real-life applications. In particular, we propose a Euclidean distance matrix completion algorithm for internet of things (IoT) network localization. In our approach, we express the Euclidean distance matrix as a function of the low rank positive semidefinite (PSD) matrix. Since the set of these PSD matrices forms a Riemannian manifold in which the notation of differentiability can be defined, we can recycle, after a proper modification, an algorithm in the Euclidean space. In order to solve the low-rank matrix completion, we propose a modified conjugate gradient algorithm, referred to as localization in Riemannian manifold using conjugate gradient (LRM-CG). We also show that the proposed LRM-CG algorithm can be easily extended to the scenario in which the observed pairwise distances are contaminated by the outliers. In fact, by modeling outliers as a sparse matrix and then adding a regularization term of the outlier matrix into the low-rank matrix completion problem, we can effectively control the outliers. From the convergence analysis, we show that LRM-CG converges linearly to the original Euclidean distance matrix under the extended Wolfes conditions. From the numerical experiments, we demonstrate that LRM-CG as well as its extended version is effective in recovering the Euclidean distance matrix. In order to solve the LRMC problem in which the desired low-rank matrix can be expressed using a graph model, we also propose a graph neural network (GNN) scheme. Our approach, referred to as graph neural network-based low-rank matrix completion (GNN-LRMC), is to use a modified convolution operation to extract the features across the graph domain. The feature data enable the training process of the proposed GNN to reconstruct the unknown entries and also optimize the graph model of the desired low-rank matrix. We demonstrate the reconstruction performance of the proposed GNN-LRMC using synthetic and real-life datasets.Abstract i Contents iii List of Tables vii List of Figures viii 1 Introduction 2 1.1 Motivation 2 1.2 Outline of the dissertation 5 2 Low-Rank Matrix Completion 6 2.1 LRMC Applications 6 2.1.1 Recommendation system 6 2.1.2 Phase retrieval 8 2.1.3 Localization in IoT networks 8 2.1.4 Image compression and restoration 10 2.1.5 Massive multiple-input multiple-output (MIMO) 12 2.1.6 Millimeter wave (mmWave) communication 12 2.2 Intrinsic Properties of LRMC 13 2.2.1 Sparsity of Observed Entries 13 2.2.2 Coherence 18 2.3 Rank Minimization Problem 22 2.4 LRMC Algorithms Without the Rank Information 25 2.4.1 Nuclear Norm Minimization (NNM) 25 2.4.2 Singular Value Thresholding (SVT) 28 2.4.3 Iteratively Reweighted Least Squares (IRLS) Minimization 31 2.5 LRMC Algorithms Using Rank Information 32 2.5.1 Greedy Techniques 34 2.5.2 Alternating Minimization Techniques 37 2.5.3 Optimization over Smooth Riemannian Manifold 39 2.5.4 Truncated NNM 41 2.6 Performance Guarantee 44 2.7 Empirical Performance Evaluation 46 2.8 Choosing the Right Matrix Completion Algorithms 55 3 IoT Localization Via LRMC 56 3.1 Problem Model 57 3.2 Optimization over Riemannian Manifold 61 3.3 Localization in Riemannian Manifold Using Conjugate Gradient (LRMCG) 66 3.4 Computational Complexity 71 3.5 Recovery Condition Analysis 73 3.5.1 Convergence of LRM-CG at Sampled Entries 73 3.5.2 Exact Recovery of Euclidean Distance Matrices 79 3.5.3 Discussion on A3 86 4 Extended LRM-CG for The Outlier Problem 92 4.1 Problem Model 94 4.2 Extended LRM-CG 94 4.3 Numerical Evaluation 97 4.3.1 Simulation Setting 98 4.3.2 Convergence Efficiency 99 4.3.3 Performance Evaluation 99 4.3.4 Outlier Problem 107 4.3.5 Real Data 107 5 LRMC Via Graph Neural Network 112 5.1 Graph Model 116 5.2 Proposed GNN-LRMC 116 5.2.1 Adaptive Model 119 5.2.2 Multilayer GNN 119 5.2.3 Output Model 122 5.2.4 Training Cost Function 123 5.3 Numerical Evaluation 123 6 Conculsion 127 A Proof of Lemma 6 129 B Proof of Theorem 7 131 C Proof of Lemma 8 134 D Proof of Theorem 9 136 E Proof of Lemma 10 140 F Proof of Lemma 12 141 G Proof of Lemma 13 142 H Proof of Lemma 14 144 I Proof of Lemma 15 146 J Proof of Lemma 17 151 K Proof of Lemma 19 154 L Proof of Lemma 20 156 M Proof of Lemma 21 158 Abstract (In Korean) 173 Acknowlegement 175Docto

Second order strategies for complementarity problems

Orientadores: Sandra Augusta Santos, Roberto AndreaniTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação CientificaResumo: Neste trabalho reformulamos o problema de complementaridade não linear generalizado (GNCP) em cones poliedrais como um sistema não linear com restrição de não negatividade em algumas variáveis, e trabalhamos na resolução de tal reformulação por meio de estratégias de pontos interiores. Em particular, definimos dois algoritmos e provamos a convergência local de tais algoritmos sob hipóteses usuais. O primeiro algoritmo é baseado no método de Newton, e o segundo, no método tensorial de Chebyshev. O algoritmo baseado no método de Chebyshev pode ser visto como um método do tipo preditor-corretor. Tal algoritmo, quando aplicado a problemas em que as funções envolvidas são afins, e com escolhas adequadas dos parâmetros, torna-se o bem conhecido algoritmo preditor-corretor de Mehrotra. Também apresentamos resultados numéricos que ilustram a competitividade de ambas as propostas.Abstract: In this work we reformulate the generalized nonlinear complementarity problem (GNCP) in polyhedral cones as a nonlinear system with nonnegativity in some variables and propose the resolution of such reformulation through interior-point methods. In particular we define two algorithms and prove the local convergence of these algorithms under standard assumptions. The first algorithm is based on Newton's method and the second, on the Chebyshev's tensorial method. The algorithm based on Chebyshev's method may be considered a predictor-corrector one. Such algorithm, when applied to problems for which the functions are affine, and the parameters are properly chosen, turns into the well-known Mehrotra's predictor corrector algorithm. We also present numerical results that illustrate the competitiveness of both proposals.DoutoradoOtimizaçãoDoutor em Matemática Aplicad

A multigrid approach to SDP relaxations of sparse polynomial optimization problems

We propose two multigrid approaches for the global optimization of polynomial op- timization problems. In our first contribution we consider problems that arise from the discretization of infinite dimensional optimization problems, such as PDE optimiza- tion problems, boundary value problems and some global optimization applications. In many of these applications, the level of discretization can be used to obtain a hierarchy of optimization models that captures the underlying infinite dimensional problem at different degrees of fidelity. This approach, inspired by multigrid methods, has been successfully used for decades to solve large systems of linear equations. However, it has not been adapted to SDP relaxations of polynomial optimization problems. The main difficulty is that the geometric information between grids is lost when the original problem is approximated via an SDP relaxation. Despite the loss of geometric infor- mation, we show how a multigrid approach can be applied by developing prolongation operators to relate the primal and dual variables of the SDP relaxation between lower and higher levels in the hierarchy of discretizations. We develop sufficient conditions for the operators to be useful in applications. Our conditions are easy to verify in prac- tice, and we discuss how they can be used to reduce the complexity of infeasible interior iv point methods. Following the same reasoning, the second approach does not assume any particular structure of the underlying polynomial problem, but instead considers the hierarchy of sparse SDP relaxations that can be obtained for any unconstrained polynomial optimizations problem with structured sparsity. Prolongation operators are defined for this type of hierarchy, and theoretical results that show their usefulness are proved. Our preliminary results highlight two promising advantages of following a multigrid approach in contrast with a pure interior point method: the percentage of problems that can be solved to a high accuracy is much higher, and the time necessary to find a solution can be reduced significantly, especially for large scale problems.Open Acces