Source code for "Sub-sampled Cubic Regularization for Non-convex Optimization", JM Kohler, A Lucchi, https://arxiv.org/abs/1705.05933
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Updated
Dec 12, 2018 - Jupyter Notebook
Source code for "Sub-sampled Cubic Regularization for Non-convex Optimization", JM Kohler, A Lucchi, https://arxiv.org/abs/1705.05933
Structured optimization in Julia
Convex fused lasso denoising with non-convex regularization and its use for pulse detection
Image Denoising using Tight-Frame with convex non-convex priors
Semi-blind deconvolution for fMRI (BOLD signal)
Python implementation of classical optimization problems
Adaptive Regularization with Cubics (ARC) optimizer for PyTorch.
Two-dimensional binary classification problem involving non-convex decision regions. The proposed solution employs a Back Propagation and a Counter Propagation Network to distinguish whether an input pattern belongs to class C1 or C2.
Julia implementation of the regularized saddle-free newton method for unconstrained non-convex optimization.
This is a C++ project that uses Windows API and OpenGL to create a graphical user interface (GUI) for drawing and manipulating 2D shapes. The project implements various algorithms for line, circle, ellipse, curve, filling, and clipping operations. The user can interact with the window using mouse only, and can choose the shape color, filling quarte
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