Trending Research
Universal Average-Case Optimality of Polyak Momentum
d2l-ai/d2l-en
•
•
Polyak momentum (PM), also known as the heavy-ball method, is a widely used optimization method that enjoys an asymptotic optimal worst-case complexity on quadratic objectives.
Optimization and Control
Rendering string diagrams recursively
zhaoolee/garss
•
•
The algebraic representation can be seen as a term of a free monoidal category or a proof tree for a small fragment of linear logic.
Category Theory Computational Geometry
Distributed Continuous-Time Optimization with Time-Varying Objective Functions and Inequality Constraints
Different from most studied distributed optimization problems with time-invariant objective functions and constraints, the optimal solution in this paper is time varying and forms a trajectory.
Optimization and Control
Physics-informed dynamic mode decomposition (piDMD)
In this work, we demonstrate how physical principles -- such as symmetries, invariances, and conservation laws -- can be integrated into the dynamic mode decomposition (DMD).
Dynamical Systems Numerical Analysis Numerical Analysis Optimization and Control Data Analysis, Statistics and Probability Fluid Dynamics
The Voice of Optimization
bstellato/mlopt
•
•
We introduce the idea that using optimal classification trees (OCTs) and optimal classification trees with-hyperplanes (OCT-Hs), interpretable machine learning algorithms developed by Bertsimas and Dunn [2017, 2018], we are able to obtain insight on the strategy behind the optimal solution in continuous and mixed-integer convex optimization problem as a function of key parameters that affect the problem.
Optimization and Control
Free Form Deformation, mesh morphing and reduced order methods: enablers for efficient aerodynamic shape optimization
mathLab/EZyRB
•
•
The proposed framework is tested on an industrially relevant application, i. e. the front-bumper morphing of the DrivAer car model, using the finite-volume method for the full-order resolution of the Reynolds-Averaged Navier-Stokes equations.
Numerical Analysis
Global Optimization via Schr{ö}dinger-F{ö}llmer Diffusion
We study the problem of finding global minimizers of $V(x):\mathbb{R}^d\rightarrow\mathbb{R}$ approximately via sampling from a probability distribution $\mu_{\sigma}$ with density $p_{\sigma}(x)=\dfrac{\exp(-V(x)/\sigma)}{\int_{\mathbb R^d} \exp(-V(y)/\sigma) dy }$ with respect to the Lebesgue measure for $\sigma \in (0, 1]$ small enough.
Optimization and Control
An Optimized, Easy-to-use, Open-source GPU Solver for Large-scale Inverse Homogenization Problems
We propose a high-performance GPU solver for inverse homogenization problems to design high-resolution 3D microstructures.
Optimization and Control
Accelerating Optimal Power Flow with GPUs: SIMD Abstraction of Nonlinear Programs and Condensed-Space Interior-Point Methods
This approach enables the specification of model equations while preserving their parallelizable structure and, in turn, facilitates the parallel AD implementation.
Optimization and Control Distributed, Parallel, and Cluster Computing
A Generalization of the Riccati Recursion for Equality-Constrained Linear Quadratic Optimal Control
This paper introduces a generalization of the well-known Riccati recursion for solving the discrete-time equality-constrained linear quadratic optimal control problem.
Optimization and Control
