We present a general-purpose solver for convex quadratic programs based on the
alternating direction method of multipliers, employing a novel operator splitting technique …

Over the past decades, the advantages of optimization-based control techniques over
conventional controllers inspired developments that enabled the use of model predictive …

Abstract We present Optimization Engine (OpEn): an open-source code generation
framework for real-time embedded nonconvex optimization, which implements a novel …

We propose a method to approximate the solution of online mixed-integer optimization (MIO)
problems at very high speed using machine learning. By exploiting the repetitive nature of …

The rise of Ethereum and other blockchains that support smart contracts has led to the
creation of decentralized exchanges (DEXs), such as Uniswap, Balancer, Curve, mStable …

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 …

Many control policies used in applications compute the input or action by solving a convex
optimization problem that depends on the current state and some parameters. Common …

Global optimization problems whose objective function is expensive to evaluate can be
solved effectively by recursively fitting a surrogate function to function samples and …

The alternating direction method of multipliers (ADMM) is a powerful operator splitting
technique for solving structured convex optimization problems. Due to its relatively low per …

In this article, we present a hierarchical multirate control architecture for nonlinear
autonomous systems operating in partially observable environments. Control objectives are …