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Found 10 papers, 8 papers with code
Computing in Operations Research using Julia
The state of numerical computing is currently characterized by a divide between highly efficient yet typically cumbersome low-level languages such as C, C++, and Fortran and highly expressive yet typically slow high-level languages such as Python and MATLAB.
Optimization and Control Numerical Analysis Programming Languages
Forward-Mode Automatic Differentiation in Julia
We present ForwardDiff, a Julia package for forward-mode automatic differentiation (AD) featuring performance competitive with low-level languages like C++.
Mathematical Software
PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulations
This work provides a brief introduction to the design of PowerModels, validates its implementation, and demonstrates its effectiveness with a proof-of-concept study analyzing five different formulations of the Optimal Power Flow problem.
Optimization and Control Computational Engineering, Finance, and Science
Extended Formulations in Mixed-integer Convex Programming
We present a unifying framework for generating extended formulations for the polyhedral outer approximations used in algorithms for mixed-integer convex programming (MICP).
Optimization and Control
Outer Approximation With Conic Certificates For Mixed-Integer Convex Problems
Using properties of the conic certificates, we show that the $\mathcal{K}^*$ cuts imply certain practically-relevant guarantees about the quality of the polyhedral relaxations, and demonstrate how to maintain helpful guarantees when the LP solver uses a positive feasibility tolerance.
Optimization and Control
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science.
JuMP: A Modeling Language for Mathematical Optimization
JuMP is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a high-level, algebraic syntax.
Optimization and Control Mathematical Software
A generic adaptive restart scheme with applications to saddle point algorithms
We provide a simple and generic adaptive restart scheme for convex optimization that is able to achieve worst-case bounds matching (up to constant multiplicative factors) optimal restart schemes that require knowledge of problem specific constants.
Optimization and Control
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs
We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler.
Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls
We investigate the optimal design of experimental studies that have pre-treatment outcome data available.
