A Nonlinear Constrained Optimization Method of MTD

solution of security constrained unit commitment problem by a new multi-objective optimization method

چکیده-پخش بار بهینه به عنوان یکی از ابزار زیر بنایی برای تحلیل سیستم های قدرت پیچیده ،برای مدت طولانی مورد بررسی قرار گرفته است.پخش بار بهینه توابع هدف یک سیستم قدرت از جمله تابع هزینه سوخت ،آلودگی ،تلفات را بهینه می کند،و هم زمان قیود سیستم قدرت را نیز برآورده می کند.در کلی ترین حالتopf یک مساله بهینه سازی غیر خطی ،غیر محدب،مقیاس بزرگ،و ایستا می باشد که می تواند شامل متغیرهای کنترلی پیوسته و گ...

A Globally Convergent Linearly Constrained Lagrangian Method for Nonlinear Optimization

For optimization problems with nonlinear constraints, linearly constrained Lagrangian (LCL) methods sequentially minimize a Lagrangian function subject to linearized constraints. These methods converge rapidly near a solution but may not be reliable from arbitrary starting points. The well known example MINOS has proven effective on many large problems. Its success motivates us to propose a glo...

Inexact Interior-Point Method for PDE-Constrained Nonlinear Optimization

Efficient Optimization of Constrained Nonlinear Resource Allocations

We present an efficient method to optimize network resource allocations under nonlinear Quality of Service (QoS) constraints. We first propose a suite of generalized proportional allocation schemes that can be obtained by minimizing the information-theoretic function of relative entropy. We then optimize over the allocation parameters, which are usually design variables an engineer can directly...

A Constrained Optimization Method for Community Detection

Abstract Community detection is one of the most important problems in complex network research. In recent years, great efforts have been devoted to this problem in term of evaluating the resulting community structure. Our previous work has shown that in addition to the resolution limit of Q, both Q and D suffer from a more serious limitation, termed as extra weak community phenomenon, i.e. some...