scholarly journals A New Approach for Kuhn-Tucker Conditions to Solve Quadratic Programming Problems with Linear Inequality Constraints

2020 ◽  
Vol 5 (5) ◽  
pp. 86
Author(s):  
Ayansola Olufemi Aderemi ◽  
Adejumo Adebowale Olusola
Keyword(s):  
Quadratic Programming ◽  
Linear Inequality ◽  
Inequality Constraints ◽  
New Approach ◽  
Linear Inequality Constraints ◽  
Quadratic Programming Problems

scholarly journals Mathematical model for determining rational machining conditions for flat grinding with the wheel periphery on machines with a rectangular table

2018 ◽  
Vol 224 ◽  
pp. 01112
Author(s):  
Dmitriy L. Skuratov ◽  
Dmitriy G. Fedorov ◽  
Dmitriy V. Evdokimov
Keyword(s):  
Mathematical Model ◽  
Objective Function ◽  
Linear Inequality ◽  
Inequality Constraints ◽  
Machining Time ◽  
Functional Parameters ◽  
Linear Inequality Constraints ◽  
Machining Quality ◽  
Machining Conditions ◽  
Grinding Operations

A mathematical model is presented for determining the rational machining conditions for flat grinding operations by the rim of a wheel on machines with a rectangular table consisting of a linear objective function and linear inequality constraints. As the objective function, the equation, determining the main machining time, was used. And constraints which are related to the functional parameters and parameters determining the machining quality and the kinematic capabilities of the machine were used as inequality constraints.

A Reference Governor for Nonlinear Systems Based on Quadratic Programming

2016 ◽  
Author(s):  
Nan I. Li ◽  
Ilya Kolmanovsky ◽  
Anouck Girard
Keyword(s):  
Nonlinear Systems ◽  
Quadratic Programming ◽  
Optimization Problem ◽  
Inequality Constraints ◽  
Closed Loop System ◽  
Linear Inequality Constraints ◽  
And Control ◽  
Bounded Disturbance ◽  
State And Control Constraints ◽  
Reference Governor

The reference governor modifies set-point commands to a closed-loop system in order to enforce state and control constraints. In this paper, we describe an approach to reference governor implementation for nonlinear systems, which is based on bounding (covering) the response of a nonlinear system by the response of a linear model with a set-bounded disturbance input. Such a design strategy is of interest as it reduces the online optimization problem to a convex quadratic programming (QP) problem with linear inequality constraints, thereby permitting standard QP solvers to be used. A numerical example is reported.

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