Nonsmooth Optimization Method and Sparsity

2013 ◽  
pp. 57-77
Author(s):  
Kazufumi Ito
Keyword(s):  
Nonsmooth Optimization ◽  
Optimization Method

Nonsmooth Optimization Method for Image Restoration

1995 ◽  
Author(s):  
Kazufumi Ito ◽  
Karl Kunisch
Keyword(s):  
Image Restoration ◽  
Nonsmooth Optimization ◽  
Numerical Optimization ◽  
Augmented Lagrangian ◽  
Energy Criterion ◽  
Optimization Methods ◽  
Optimization Method ◽  
Augmented Lagrangian Methods ◽  
Active Set ◽  
First Order

Abstract In this paper we discuss applications of the numerical optimization methods for nonsmooth optimization, developed in [IK1] for the variational formulation of image restoration problems involving bounded variation type energy criterion. The Uzawa’s algorithm, first order augmented Lagrangian methods and Newton-like update using the active set strategy are described.

Nonsmooth Optimization Method for VLSI Global Placement

2015 ◽  
Vol 34 (4) ◽  
pp. 642-655 ◽  
Author(s):  
Wenxing Zhu ◽  
Jianli Chen ◽  
Zheng Peng ◽  
Genghua Fan
Keyword(s):  
Nonsmooth Optimization ◽  
Optimization Method ◽  
Global Placement

Nonsmooth Optimization Method for H∞ Output Feedback Control

2019 ◽  
Vol 36 (03) ◽  
pp. 1950015
Author(s):  
Qiong Wu ◽  
Jin-He Wang ◽  
Hong-Wei Zhang ◽  
Shuang Wang ◽  
Li-Ping Pang
Keyword(s):  
Feedback Control ◽  
Nonsmooth Optimization ◽  
Output Feedback ◽  
Closed Loop ◽  
Linear Time ◽  
Output Feedback Control ◽  
Initial Point ◽  
Optimization Method ◽  
Linear Time Invariant ◽  
Loop Transfer Function

This paper proposes a nonsmooth optimization method for [Formula: see text] output feedback control problem of linear time-invariant(LTI) systems based on bundle technique. We formulate this problem as a nonconvex and nonsmooth semi-infinite constrained optimization problem by quantifying both internal stability of closed-loop system and measurement of system performance, where [Formula: see text] norm of closed-loop transfer function and a stabilization channel is used. Our method uses progress function and bundle technique to solve the resulting problem which has a composite structure. We prove the convergence to a critical point from a feasible initial point and test some benchmarks to demonstrate the effectiveness of this method.

Optimization Method of Superelevation Transition for Oval Racing Tracks

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Yuchen Wang ◽  
Tao Lu ◽  
Hongxing Zhao ◽  
Zhiying Bao
Keyword(s):  
Optimization Method

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Dimensioning a recovery boiler furnace using mathematical optimization

TAPPI Journal ◽  
2015 ◽  
Vol 14 (2) ◽  
pp. 119-129 ◽  
Author(s):  
VILJAMI MAAKALA ◽  
PASI MIIKKULAINEN
Keyword(s):  
Radial Basis Function Network ◽  
High Capacity ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Performance Criteria ◽  
Boiler Furnace ◽  
Starting Point ◽  
Recovery Boiler ◽  
Base Design

Capacities of the largest new recovery boilers are steadily rising, and there is every reason to expect this trend to continue. However, the furnace designs for these large boilers have not been optimized and, in general, are based on semiheuristic rules and experience with smaller boilers. We present a multiobjective optimization code suitable for diverse optimization tasks and use it to dimension a high-capacity recovery boiler furnace. The objective was to find the furnace dimensions (width, depth, and height) that optimize eight performance criteria while satisfying additional inequality constraints. The optimization procedure was carried out in a fully automatic manner by means of the code, which is based on a genetic algorithm optimization method and a radial basis function network surrogate model. The code was coupled with a recovery boiler furnace computational fluid dynamics model that was used to obtain performance information on the individual furnace designs considered. The optimization code found numerous furnace geometries that deliver better performance than the base design, which was taken as a starting point. We propose one of these as a better design for the high-capacity recovery boiler. In particular, the proposed design reduces the number of liquor particles landing on the walls by 37%, the average carbon monoxide (CO) content at nose level by 81%, and the regions of high CO content at nose level by 78% from the values obtained with the base design. We show that optimizing the furnace design can significantly improve recovery boiler performance.

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