This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Networks(PINNs)"
Derivation and construction of a thermodynamic phase diagram for ternary alloy systems with 2 or 3 phases
Gitcoin Bounty - Open Defi Hackathon Submission for Parallel Swap in Ref Finance on Near.
Notes from Mathematics for Machine Learning and Data Science Specialization
Implementation of Support Vector Machine algorithm using Lagrange Multipliers method for solving non-linear constrained optimization problems.
EPITA Course Materials
DRIP Numerical Optimizer is a collection of Java libraries for Numerical Optimization and Spline Functionality.
Machine Learning for Data 3141 Reichman University Spring 2022 - 6 Homework Projects
A Lagrange Dual Learning Framework for Solving Constrained Inverse Kinematics Tasks (Project for 6.881 Spring 2020, Optimization for Machine Learning)
In this project, we modeled the motion of a ball on a rotating parabolic wire using the Lagrange multiplier method. We derived and solved the system of differential equations via the Constraint Stabilization method, analyzing the results using Python with NumPy, SciPy, and Matplotlib for computation and visualization.
A collection of tools to used to evaluate dynamical systems with nonholonomic constraints.
A foray into constrained optimization as a final project.
Reprodução didática em C/C++ do artigo de Parreira et al. (2006) sobre o método Element-Free Galerkin aplicado a problemas eletromagnéticos tridimensionais, com tradução comentada, formulação matemática, testes numéricos e comparação com FEM.
Optimize delivery fuel usage using Lagrange Multipliers in Python.
SM Model Optimization: Lagrange and MIP
An experimental optimization project comparing classical and metaheuristic methods, applying a modified legacy EvoloPy framework to wrapper-based feature selection, and evaluating PSO with stagnation-aware early stopping.
Graph-Lagrange is an interactive Python tool for analyzing and visualizing mathematical functions in 1D and 2D. It allows users to compute critical points, local maxima, minima, saddle points, inflection points, and limits for single-variable and multivariable functions. The project also supports constrained optimization using Lagrange multipliers.
Material from the course of Static and Dynamic Optimization at ENSEM - Université de Lorraine.
Lagrange Multipliers in Portfolio Optimization with Mixed Constraints
Add a description, image, and links to the lagrange-multipliers topic page so that developers can more easily learn about it.
To associate your repository with the lagrange-multipliers topic, visit your repo's landing page and select "manage topics."