Results 71 to 80 of about 330,664 (214)
Applied Sciences, 2018 This paper proposes a convex quadratic optimization commutation method to raise the equalization degree of power loss distribution of coil array for magnetic levitation planar motors.
Shengguo Zhang +2 more
doaj +1 more source
Senior Thesis for Haverford College Convex Optimization, Newton's Method and Interior Point Method [PDF]
arXiv, 2020 This paper consists of four general parts: convex sets; convex functions; convex optimization; and the interior-point algorithm. I will start by introducing the definition of convex sets and give three common convex set examples which will be used later in this paper, then prove the significant separating and supporting hyperplane theorems.
arxiv
Chasing Convex Bodies Optimally [PDF]
, 2020 In the chasing convex bodies problem, an online player receives a request sequence of $N$ convex sets $K_1,\dots, K_N$ contained in a normed space $\mathbb R^d$. The player starts at $x_0\in \mathbb R^d$, and after observing each $K_n$ picks a new point $x_n\in K_n$. At each step the player pays a movement cost of $||x_n-x_{n-1}||$.
openaire +2 more sources
Large Scale Resource Allocation for the Internet of Things Network Based on ADMM
IEEE Access, 2020 Large scale deployment of Internet of Things (IoT) devices poses challenges in resource allocation. In this paper, alternating direction method of multipliers (ADMM) is adopted to solve such large scale resource allocation problems.
Yanhua He +3 more
doaj +1 more source
AIMS MathematicsWe considered a convex bilevel optimization problem when the outer level problem was to find a minimizer of a strongly convex function over the set of solutions of the inner level problem which was in the form of minimization of the sum of a convex ...
Adisak Hanjing +2 more
doaj +1 more source
An optimal algorithm for stochastic strongly-convex optimization [PDF]
arXiv, 2010 We consider stochastic convex optimization with a strongly convex (but not necessarily smooth) objective. We give an algorithm which performs only gradient updates with optimal rate of convergence.
arxiv
Regrets of Proximal Method of Multipliers for Online Non-convex Optimization with Long Term Constraints [PDF]
arXiv, 2022 The online optimization problem with non-convex loss functions over a closed convex set, coupled with a set of inequality (possibly non-convex) constraints is a challenging online learning problem. A proximal method of multipliers with quadratic approximations (named as OPMM) is presented to solve this online non-convex optimization with long term ...
arxiv
Journal of the Mechanical Behavior of Materials, 2015 This paper introduces constructing convex-relaxed programs for nonconvex optimization problems. Branch-and-bound algorithms are convex-relaxation-based techniques.
Keller André A.
doaj +1 more source
Nearly convex sets: fine properties and domains or ranges of subdifferentials of convex functions [PDF]
arXiv, 2015 Nearly convex sets play important roles in convex analysis, optimization and theory of monotone operators. We give a systematic study of nearly convex sets, and construct examples of subdifferentials of lower semicontinuous convex functions whose domain or ranges are nonconvex.
arxiv
RSS-TOA based localization algorithm in non-line-of-sight environment
Dianxin kexue, 2019 The existence of non-line-of-sight (NLOS) error can degrade the positioning performance in wireless sensor network localization system,especially in indoor localization.To overcome the localization error caused by NLOS propagation,a localization ...
Zhigang LU +4 more
doaj +2 more sources