Liberating the Subgradient Optimality Conditions from Constraint Qualifications

In convex optimization the significance of constraint qualifications is evidenced by the simple duality theory, and the elegant subgradient optimality conditions which completely characterize a minimizer. However, the constraint qualifications do not always hold even for finite dimensional optimization problems and frequently fail for infinite dimensional problems. In the present work we take a broader view of the subgradient optimality conditions by allowing them to depend on a sequence of ε-subgradients at a minimizer and then by letting them to hold in the limit. Liberating the optimality conditions in this way permits us to obtain a complete characterization of optimality without a constraint qualification. As an easy consequence of these results we obtain optimality conditions for conic convex optimization problems without a constraint qualification. We derive these conditions by applying a powerful combination of conjugate analysis and ε-subdifferential calculus. Numerical examples are discussed to illustrate the significance of the sequential conditions.     

Global Optimization using a Dynamical Systems Approach

We develop new algorithms for global optimization by combining well known branch and bound methods with multilevel subdivision techniques for the computation of invariant sets of dynamical systems. The basic idea is to view iteration schemes for local optimization problems – e.g. Newton’s method or conjugate gradient methods – as dynamical systems and to compute set coverings of their fixed points. The combination with bounding techniques allow for the computation of coverings of the global optima only. We show convergence of the new algorithms and present a particular implementation. Michael Dellnitz - Research of the authors is partially supported by the Deutsche Forschungsgemeinschaft within the Sonderforschungsbereich 376     

On Finite Element Approximations to a Shape Gradient Flow in Shape Optimization of Elliptic Problems

Shape gradient flows are widely used in numerical shape optimization algorithms. We investigate the accuracy and effectiveness of approximate shape gradients flows for shape optimization of elliptic problems. We present convergence analysis with a priori error estimates for finite element approximations of shape gradient flows associated with a distributed or boundary expression of Eulerian derivative. Numerical examples are presented to verify theory and show that using the volume expression is effective for shape optimization with Dirichlet and Neumann boundary conditions.     

On mixed finite element approximations of shape gradients in shape optimization with the Navier–Stokes equation

For shape optimization of fluid flows governed by the Navier–Stokes equation, we investigate effectiveness of shape gradient algorithms by analyzing convergence and accuracy of mixed finite element approximations to both the distributed and boundary types of shape gradients. We present convergence analysis with a priori error estimates for the two approximate shape gradients. The theoretical analysis shows that the distributed formulation has superconvergence property. Numerical results with comparisons are presented to verify theory and show that the shape gradient algorithm based on the distributed formulation is highly effective and robust for shape optimization.     

Tensor train completion: Local recovery guarantees via Riemannian optimization

In this work, we estimate the number of randomly selected elements of a tensor that with high probability guarantees local convergence of Riemannian gradient descent for tensor train completion. We derive a new bound for the orthogonal projections onto the tangent spaces based on the harmonic mean of the unfoldings' singular values and introduce a notion of core coherence for tensor trains. We also extend the results to tensor train completion with auxiliary subspace information and obtain the corresponding local convergence guarantees.     

A deep neural network-based method for solving obstacle problems

In this paper, we propose a method based on deep neural networks to solve obstacle problems. By introducing penalty terms, we reformulate the obstacle problem as a minimization optimization problem and utilize a deep neural network to approximate its solution. The convergence analysis is established by decomposing the error into three parts: approximation error, statistical error and optimization error. The approximate error is bounded by the depth and width of the network, the statistical error is estimated by the number of samples, and the optimization error is reflected in the empirical loss term. Due to its unsupervised and meshless advantages, the proposed method has wide applicability. Numerical experiments illustrate the effectiveness and robustness of the proposed method and verify the theoretical proof.     

Energy-efficient beamforming optimization for MISO communication based on reconfigurable intelligent surface

Reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiency (EE) by reconfiguring the wireless propagation environment. In this paper, we propose a base station (BS) beamforming and RIS phase shift optimization technique that maximizes the EE of a RIS-aided multiple-input–single-output system. In particular, considering the system circuits’ energy consumption, an EE maximization problem is formulated by jointly optimizing the active beamforming at the BS and the passive beamforming at the RIS, under the constraints of each user’ rate requirement, the BS’s maximal transmit power budget and unit-modulus constraint of the RIS phase shifts. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. To overcome this obstacle, we divide the problem into active and passive beamforming optimization subproblems. For the first subproblem, the active beamforming is given by the maximum ratio transmission optimal strategy. For the second subproblem, the optimal phase shift matrix at the RIS is obtained by exploiting sine cosine algorithm (SCA). Moreover, for this case where each reflection element’s working state is controlled by a circuit switch, each reflection element’s switch value is optimized with the aid of particle swarm optimization algorithm. Finally, numerical results verify the effectiveness of our proposed algorithm compared to other algorithms.     

优化设计理论在抗滑桩工程中的应用

采用数学规划方法中的直接最优化方法———复合形法,对已知荷载或内力的抗滑桩,以结构成本为目标函数进行满足工程使用要求的有约束最优化设计,编制了相应的程序,并用VisualBasic6 .0开发了应用程序界面,将优化以后所得结果与使用传统设计方法结果比较,有较大降低结构成本的效果。     

The topological optimization for truss structures with stress constraints based on the exist-null combined model

A new exist-null combined model is proposed for the structural topology optimization. The model is applied to the topology optimization of the truss with stress constraints. Satisfactory computational result can be obtained with more rapid and more stable convergence as compared with the cross-sectional optimization. This work also shows that the presence of independent and continuous topological variable motivates the research of structural topology optimization. The project supported by the State Key Laboratory for Structural Analysis of Industrial Equipment, Dalian University of Technology.     

对实心弹性薄板优化的进一步讨论

回顾了实心弹性薄板优化研究的发展历程。给出了由无限密和无限细的肋骨加强的环板在常肋骨密度时的解析解；采用精确刚度阵的有限元法计算了具有有限根肋骨加强的板的柔顺性，它低于光滑优化解的柔顺性。这些结果进一步说明近期文献中出现的枕头形的光滑解并不是几何受约束实心弹性薄板优化问题的全局最优解。