OSQP: An operator splitting solver for quadratic programs
We present a general-purpose solver for convex quadratic programs based on the
alternating direction method of multipliers, employing a novel operator splitting technique …
alternating direction method of multipliers, employing a novel operator splitting technique …
Embedded optimization methods for industrial automatic control
Starting in the late 1970s, optimization-based control has built up an impressive track record
of successful industrial applications, in particular in the petrochemical and process …
of successful industrial applications, in particular in the petrochemical and process …
Proximal splitting algorithms for convex optimization: A tour of recent advances, with new twists
Convex nonsmooth optimization problems, whose solutions live in very high dimensional
spaces, have become ubiquitous. To solve them, the class of first-order algorithms known as …
spaces, have become ubiquitous. To solve them, the class of first-order algorithms known as …
Infeasibility detection in the alternating direction method of multipliers for convex optimization
The alternating direction method of multipliers is a powerful operator splitting technique for
solving structured optimization problems. For convex optimization problems, it is well known …
solving structured optimization problems. For convex optimization problems, it is well known …
Operator splitting for a homogeneous embedding of the linear complementarity problem
B O'Donoghue - SIAM Journal on Optimization, 2021 - SIAM
We present a first-order quadratic cone programming algorithm that can scale to very large
problem sizes and produce modest accuracy solutions quickly. Our algorithm returns primal …
problem sizes and produce modest accuracy solutions quickly. Our algorithm returns primal …
Using stochastic programming to train neural network approximation of nonlinear MPC laws
To facilitate the real-time implementation of nonlinear model predictive control (NMPC), this
paper proposes a deep learning-based NMPC scheme, in which the NMPC law is …
paper proposes a deep learning-based NMPC scheme, in which the NMPC law is …
RandProx: Primal-dual optimization algorithms with randomized proximal updates
L Condat, P Richtárik - arXiv preprint arXiv:2207.12891, 2022 - arxiv.org
Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization
problems, in particular those arising in machine learning. We propose a new primal-dual …
problems, in particular those arising in machine learning. We propose a new primal-dual …
Proximal gradient algorithms under local Lipschitz gradient continuity: A convergence and robustness analysis of PANOC
A De Marchi, A Themelis - Journal of Optimization Theory and Applications, 2022 - Springer
Composite optimization offers a powerful modeling tool for a variety of applications and is
often numerically solved by means of proximal gradient methods. In this paper, we consider …
often numerically solved by means of proximal gradient methods. In this paper, we consider …
Dualize, split, randomize: Toward fast nonsmooth optimization algorithms
We consider minimizing the sum of three convex functions, where the first one F is smooth,
the second one is nonsmooth and proximable and the third one is the composition of a …
the second one is nonsmooth and proximable and the third one is the composition of a …
An optimal algorithm for strongly convex minimization under affine constraints
Optimization problems under affine constraints appear in various areas of machine learning.
We consider the task of minimizing a smooth strongly convex function F (x) under the affine …
We consider the task of minimizing a smooth strongly convex function F (x) under the affine …