Penalty dual decomposition method for nonsmooth nonconvex optimization—Part I: Algorithms and convergence analysis
Many contemporary signal processing, machine learning and wireless communication
applications can be formulated as nonconvex nonsmooth optimization problems. Often there …
applications can be formulated as nonconvex nonsmooth optimization problems. Often there …
Applications of Lagrangian relaxation-based algorithms to industrial scheduling problems, especially in production workshop scenarios: A review
L Sun, R Yang, J Feng, G Guo - Journal of Process Control, 2024 - Elsevier
Industrial scheduling problems (ISPs), especially industrial production workshop scheduling
problems (IPWSPs) in various sectors like manufacturing, and power require allocating …
problems (IPWSPs) in various sectors like manufacturing, and power require allocating …
Stabilized SQP revisited
AF Izmailov, MV Solodov - Mathematical programming, 2012 - Springer
The stabilized version of the sequential quadratic programming algorithm (sSQP) had been
developed in order to achieve superlinear convergence in situations when the Lagrange …
developed in order to achieve superlinear convergence in situations when the Lagrange …
Global convergence of augmented Lagrangian methods applied to optimization problems with degenerate constraints, including problems with complementarity …
AF Izmailov, MV Solodov, EI Uskov - SIAM Journal on Optimization, 2012 - SIAM
We consider global convergence properties of the augmented Lagrangian methods on
problems with degenerate constraints, with a special emphasis on mathematical programs …
problems with degenerate constraints, with a special emphasis on mathematical programs …
A globally convergent stabilized SQP method
PE Gill, DP Robinson - SIAM Journal on Optimization, 2013 - SIAM
Sequential quadratic programming (SQP) methods are a popular class of methods for
nonlinearly constrained optimization. They are particularly effective for solving a sequence …
nonlinearly constrained optimization. They are particularly effective for solving a sequence …
An adaptive augmented Lagrangian method for large-scale constrained optimization
We propose an augmented Lagrangian algorithm for solving large-scale constrained
optimization problems. The novel feature of the algorithm is an adaptive update for the …
optimization problems. The novel feature of the algorithm is an adaptive update for the …
A stabilized SQP method: global convergence
PE Gill, V Kungurtsev… - IMA Journal of Numerical …, 2017 - academic.oup.com
Stabilized sequential quadratic programming (SQP) methods for nonlinear optimization are
designed to provide a sequence of iterates with fast local convergence even when the active …
designed to provide a sequence of iterates with fast local convergence even when the active …
Penalty dual decomposition method with application in signal processing
Many problems of recent interest in signal processing, machine learning and wireless
communications can be posed as nonconvex nonsmooth optimization problems. These …
communications can be posed as nonconvex nonsmooth optimization problems. These …
NMPC through qLPV embedding: A tutorial review of different approaches
Abstract Nonlinear Model Predictive Control (NMPC) formulations through quasi-Linear
Parameter Varying (qLPV) embeddings have been brought to focus in recent literature. The …
Parameter Varying (qLPV) embeddings have been brought to focus in recent literature. The …
A stabilized SQP method: superlinear convergence
Stabilized sequential quadratic programming (sSQP) methods for nonlinear optimization
generate a sequence of iterates with fast local convergence regardless of whether or not the …
generate a sequence of iterates with fast local convergence regardless of whether or not the …