2019
DOI: 10.1007/978-3-030-21803-4_3
|View full text |Cite
|
Sign up to set email alerts
|

A Sequential Linear Programming Algorithm for Continuous and Mixed-Integer Nonconvex Quadratic Programming

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2021
2021
2021
2021

Publication Types

Select...
2

Relationship

2
0

Authors

Journals

citations
Cited by 2 publications
(3 citation statements)
references
References 12 publications
0
3
0
Order By: Relevance
“…The obtained numerical simulation results on four convex and non-convex linearquadratic optimal control problems arising in mechanics show that the new direct method converges fastly to the optimal value of the quality criterion found by the analytical method. In a future work, we will apply the algorithm developed in [4] to efficiently solve the non-convex quadratic programming problems obtained by the discretization methods presented in this work. Furthermore, we will adapt the proposed algorithm for solving linear-quadratic optimal control problems, where the matrices of the linear dynamical system depend on the time.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The obtained numerical simulation results on four convex and non-convex linearquadratic optimal control problems arising in mechanics show that the new direct method converges fastly to the optimal value of the quality criterion found by the analytical method. In a future work, we will apply the algorithm developed in [4] to efficiently solve the non-convex quadratic programming problems obtained by the discretization methods presented in this work. Furthermore, we will adapt the proposed algorithm for solving linear-quadratic optimal control problems, where the matrices of the linear dynamical system depend on the time.…”
Section: Discussionmentioning
confidence: 99%
“…However, ASM can give local solutions which are not global for other non-convex test-problems not considered in this work. So in order to find a good approximate global optimal solution for the non-convex quadratic programming problems of the form (13), we can use the sequential linear programming algorithm developed in [4] for solving non-convex quadratic programs.…”
Section: Examplementioning
confidence: 99%
“…• Note that this version of SLPA is an improved version of that proposed in [4]. Moreover, in [7,11] the denominators of the numbers α j can be equal to zero when u j = x (see Formula ( 5)).…”
Section: The Successive Linear Programming Algorithm For Continuous Nonconvex Qpmentioning
confidence: 99%