A topology optimization method for electric machines and devices through submodular maximization
نویسندگان
چکیده
منابع مشابه
A BINARY LEVEL SET METHOD FOR STRUCTURAL TOPOLOGY OPTIMIZATION
This paper proposes an effective algorithm based on the level set method (LSM) to solve shape and topology optimization problems. Since the conventional LSM has several limitations, a binary level set method (BLSM) is used instead. In the BLSM, the level set function can only take 1 and -1 values at convergence. Thus, it is related to phase-field methods. We don’t need to solve the Hamilton-Jac...
متن کاملTopology optimization of electric machines based on topological sensitivity analysis
Topological sensitivities are a very useful tool for determining optimal designs. The topological derivative of a domain-dependent functional represents the sensitivity with respect to the insertion of an infinitesimally small hole. In the gradient-based ON/OFF method, proposed by M. Ohtake, Y. Okamoto and N. Takahashi in 2005, sensitivities of the functional with respect to a local variation o...
متن کاملConditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap
In this paper, we study the problem of constrained and stochastic continuous submodular maximization. Even though the objective function is not concave (nor convex) and is defined in terms of an expectation, we develop a variant of the conditional gradient method, called Stochastic Continuous Greedy, which achieves a tight approximation guarantee. More precisely, for a monotone and continuous D...
متن کاملDerandomization for k-submodular maximization
Submodularity is one of the most important property of combinatorial optimization, and k-submodularity is a generalization of submodularity. Maximization of a k-submodular function is NP-hard, and approximation algorithm has been studied. For monotone k-submodular functions, [Iwata, Tanigawa, and Yoshida 2016] gave k/(2k−1)-approximation algorithm. In this paper, we give a deterministic algorit...
متن کاملGradient Methods for Submodular Maximization
In this paper, we study the problem of maximizing continuous submodular functions that naturally arise in many learning applications such as those involving utility functions in active learning and sensing, matrix approximations and network inference. Despite the apparent lack of convexity in such functions, we prove that stochastic projected gradient methods can provide strong approximation gu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronics and Communications in Japan
سال: 2019
ISSN: 1942-9533,1942-9541
DOI: 10.1002/ecj.12173