scholarly journals Initializing Successive Linear Programming Solver for ACOPF using Machine Learning

2021 ◽  
Author(s):  
Sayed Abdullah Sadat ◽  
Mostafa Sahraei-Ardakani
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Successive Linear Programming ◽  
Linear Programming Solver

Nonlinear Optimization by Successive Linear Programming

1982 ◽  
Vol 28 (10) ◽  
pp. 1106-1120 ◽  
Author(s):  
F. Palacios-Gomez ◽  
L. Lasdon ◽  
M. Engquist
Keyword(s):  
Linear Programming ◽  
Nonlinear Optimization ◽  
Successive Linear Programming

scholarly journals Calculation of interval damping ratio under uncertain load in power system

2012 ◽  
Vol 60 (1) ◽  
pp. 151-158
Author(s):  
J. Xing ◽  
C. Chen ◽  
P. Wu
Keyword(s):  
Linear Programming ◽  
Power System ◽  
Optimization Model ◽  
Damping Ratio ◽  
Small Signal ◽  
Small Signal Stability ◽  
Signal Stability ◽  
Successive Linear Programming ◽  
Calculation Results ◽  
Lower Limits

Calculation of interval damping ratio under uncertain load in power system The problem of small-signal stability considering load uncertainty in power system is investigated. Firstly, this paper shows attempts to create a nonlinear optimization model for solving the upper and lower limits of the oscillation mode's damping ratio under an interval load. Then, the effective successive linear programming (SLP) method is proposed to solve this problem. By using this method, the interval damping ratio and corresponding load states at its interval limits are obtained. Calculation results can be used to evaluate the influence of load variation on a certain mode and give useful information for improvement. Finally, the proposed method is validated on two test systems.

scholarly journals Linear programming measures for solving the problem of a linear division of convex multiplayers.

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Sarmad H. Ali ◽  
Osamah A. Ali ◽  
Samir C. Ajmi
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Optimal Solution ◽  
Optimal Method ◽  
Linear Separation ◽  
Different Types ◽  
Simplex Methods

In this research, we are trying to solve Simplex methods which are used for successively improving solution and finding the optimal solution, by using different types of methods Linear, the concept of linear separation is widely used in the study of machine learning, through this study we will find the optimal method to solve by comparing the time consumed by both Quadric and Fisher methods.

scholarly journals Convergence Analysis on an Accelerated Proximal Point Algorithm for Linearly Constrained Optimization Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sha Lu ◽  
Zengxin Wei
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Proximal Point Algorithm ◽  
Original Problem ◽  
Optimization Problems ◽  
Linear Constraints ◽  
Proximal Point ◽  
Constrained Optimization Problems ◽  
Linearly Constrained ◽  
Linear Programming Problems

Proximal point algorithm is a type of method widely used in solving optimization problems and some practical problems such as machine learning in recent years. In this paper, a framework of accelerated proximal point algorithm is presented for convex minimization with linear constraints. The algorithm can be seen as an extension to G u ¨ ler’s methods for unconstrained optimization and linear programming problems. We prove that the sequence generated by the algorithm converges to a KKT solution of the original problem under appropriate conditions with the convergence rate of O 1 / k 2 .

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