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abs-normal Public
Tools in Julia for solving equations and optimization problems involving piecewise linear/affine functions expressed in "abs-normal form". Uses new formulations by Zhang and Khan (2024).
Julia MIT License UpdatedOct 29, 2024 -
ConvexSampling.jl Public
Constructs linear underestimators of convex functions by tractable black-box sampling. Implemented in Julia.
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convex-ode-subgradients Public
Contains code for the numerical examples in an article by Song and Khan (2024). This code evaluates subgradients for convex relaxations of parametric ordinary differential equations (ODEs)
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NonogramSolver.jl Public
Formulates and solves nonogram puzzles (a.k.a. Picross and paint-by-number) in Julia. Uses a new integer linear programming formulation, and solves it with JuMP.
Julia MIT License UpdatedApr 18, 2024 -
continuous-convex-adjoints Public
A Julia/C++ implementation and numerical examples of adjoint subgradient evaluation for convex ODE relaxations, as described by Zhang and Khan (2024).
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nonogram-ilp Public
ILP formulation in GAMS for solving nonograms
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nonsmooth-forward-ad Public
Implementation of the vector forward mode of automatic differentiation (AD) for generalized differentiation of nonsmooth functions. Uses operator overloading in Julia.
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implicit-func-relaxations Public
Proof-of-concept implementation of a new method for computing convex relaxations for implicit functions and inverse functions.
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ob-ode-relaxations Public
Proof-of-concept implementation of a method by Song and Khan (2022) for computing convex relaxations for parametric ordinary differential equations.
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computational-graph-tools Public
Tools in Julia for automatically constructing the computational graph/tape of a composite function, and performing the reverse AD mode.
Julia MIT License UpdatedAug 17, 2022
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