An interactive satisficing approach for solving fuzzy multiobjective Linear optimization problems based on the Attainable Reference Point Method

2010 ◽  
Vol 5 (5) ◽  
pp. 1-15
Author(s):  
Taghreed Hassan ◽  
A. Mousa
Keyword(s):  
Reference Point ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Point Method ◽  
Reference Point Method ◽  
Linear Optimization Problems

scholarly journals Certified POD-Based Multiobjective Optimal Control of Time-Variant Heat Phenomena

2018 ◽  
Author(s):  
Stefan Banholzer ◽  
Eugen Makarov ◽  
Stefan Volkwein
Keyword(s):  
Optimal Control ◽  
Reference Point ◽  
Model Order Reduction ◽  
Optimization Problems ◽  
Order Reduction ◽  
Point Method ◽  
Model Order ◽  
Reference Point Method ◽  
Multiobjective Optimal Control ◽  
Proper Orthogonal

Many optimization problems in applications can be formulated using several objective functions, which are conflicting with each other. This leads to the notion of multiobjective or multicriterial optimization problems. Here, we investigate the application of the Euclidean reference point method in combination with model-order reduction to multiobjective optimal control problems. Since the reference point method transforms the multiobjective optimal control problem into a series of scalar optimization problems, the method of proper orthogonal decomposition (POD) is introduced as an approach for model-order reduction.

scholarly journals A Full Nesterov-Todd Step Infeasible Interior-point Method for Symmetric Optimization in the Wider Neighborhood of the Central Path

2021 ◽  
Vol 9 (2) ◽  
pp. 250-267
Author(s):  
Lesaja Goran ◽  
G.Q. Wang ◽  
A. Oganian
Keyword(s):  
Interior Point ◽  
Interior Point Method ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Point Method ◽  
Central Path ◽  
Symmetric Optimization ◽  
Statistical Disclosure Limitation ◽  
Starting Point ◽  
Linear Optimization Problems

In this paper, an improved Interior-Point Method (IPM) for solving symmetric optimization problems is presented. Symmetric optimization (SO) problems are linear optimization problems over symmetric cones. In particular, the method can be efficiently applied to an important instance of SO, a Controlled Tabular Adjustment (CTA) problem which is a method used for Statistical Disclosure Limitation (SDL) of tabular data. The presented method is a full Nesterov-Todd step infeasible IPM for SO. The algorithm converges to ε-approximate solution from any starting point whether feasible or infeasible. Each iteration consists of the feasibility step and several centering steps, however, the iterates are obtained in the wider neighborhood of the central path in comparison to the similar algorithms of this type which is the main improvement of the method. However, the currently best known iteration bound known for infeasible short-step methods is still achieved.

Sign in / Sign up

Export Citation Format

Share Document