The Long-Run Distribution of Stochastic Gradient Descent: A Large Deviations Approach
Explore the long-run behavior of stochastic gradient descent in non-convex problems, analyzing state space visitation patterns and energy level distributions.
A New Derivative-Free Method Using an Improved Under-Determined Quadratic Interpolation Model
Explore a novel derivative-free optimization method using improved under-determined quadratic interpolation, considering trust-region iteration properties and model optimality.
Stochastic Algorithms for Constrained Continuous Optimization
Explore stochastic-gradient-based algorithms for constrained continuous optimization, focusing on interior-point and sequential-quadratic-programming methods with convergence guarantees and practical applications.
Revisiting Inexact Fixed-Point Iterations for Min-Max Problems - Stochasticity and Cohypomonotonicity
Explore advanced optimization techniques for constrained min-max problems, focusing on inexact fixed-point iterations and stochastic algorithms for nonconvex-nonconcave scenarios.
A Fresh Look at Algorithms for Solving Smooth Multiobjective Optimization Problems
Explore new algorithms for smooth multiobjective optimization using linear programming to find decreasing directions, applicable to various constraint types and yielding substationary points.
Localization Phenomena in Interacting Flatland Systems
Explore localization phenomena in flatband networks, examining compact localized eigenstates and the impact of perturbations. Discover finetuning protocols for diverse localization features in classical and quantum systems.
Fun with Functions: Designing Fast Math Approximations with Python
Explore fast math approximation techniques for audio using Python. Learn Taylor polynomials, Padé approximants, and minimax methods to optimize computations while balancing accuracy and speed.
cadCAD.jl - A Modeling Library for Generalized Dynamical Systems
Explore generalized dynamical systems modeling with cadCAD.jl. Learn to simulate complex systems, evolve data structures, and gain iterative insights for enhanced system engineering and data science workflows.
Meta Learning Control Variates for Variance Reduction with Limited Data
Explore meta-learning control variates for variance reduction in Monte Carlo estimators with limited data, leveraging similarities across multiple integration tasks for improved performance.
Integrating and Plotting Equations of Motion - CE2120 Homework 01
Integrate and plot equations of motion for engineering applications. Master techniques for solving and visualizing dynamic systems.
Model Reparametrization with StructuralIdentifiability.jl - JuliaCon 2024
Explore StructuralIdentifiability.jl's new features for rapid identification of identifiable functions and model reparametrization, enhancing modeling and control practices.
Combined Stochastic Models for Cancer Patient Trajectories
Integrating diverse clinical data into a unified stochastic model for cancer patient trajectories, combining tumor growth, metastasis, and survival data using Julia for simulation and Bayesian inference.
Real Time ODEs for Sound Synthesis and Parameter Exploration - JuliaCon 2024
Explore real-time ODEs for sound synthesis and parameter analysis using Julia, combining visual and auditory representations to unveil dynamical system behaviors and control parameters.
PiecewiseAffineApprox.jl - Automated Piecewise Affine Convex Approximations for Optimization Models
Explore piecewise affine convex approximations for optimization models using PiecewiseAffineApprox.jl, featuring flexible options and methods for best-fit calculations in Julia.
Differentiating Parametric JuMP Models in Julia
Explore differentiating parametric JuMP models, combining DiffOpt and ParametricOptInterface for efficient optimization and parameter sensitivity analysis in Julia.
Teaching Optimization: Comparing cvxpy, pyomo, and JuMP
Explore convex optimization modeling frameworks, comparing cvxpy, pyomo, and JuMP. Learn about common student challenges and how JuMP addresses them in undergraduate and graduate courses.
Applied Optimization with JuMP at SINTEF
Explore JuMP for flexible optimization modeling at SINTEF, focusing on hydrogen infrastructure and circular logistics. Learn about complementary packages enhancing JuMP's functionality.
Monte Carlo Pi Methods: Technical Editing - Lecture ME3256
Enhance technical writing skills for Monte Carlo pi methods. Learn effective editing techniques to improve clarity and precision in scientific reports.
Current Fluctuations in Noninteracting Run-and-Tumble Particles in One Dimension
Explore current fluctuations in noninteracting run-and-tumble particles, focusing on flux distribution through origin and anomalous large-deviation form in active particles.
Gauged Gaussian Fermionic PEPS: A Tool for Studying Lattice Gauge Theories
Explore lattice gauge theories using Gauged Gaussian Fermionic PEPS, a tensor network method for efficient numerical computations without sign problems in multiple space dimensions.
Primal-Dual Optimization Methods for Robust Machine Learning
Explore primal-dual optimization techniques for enhancing machine learning robustness, focusing on advanced mathematical approaches to improve model performance and reliability.