skscope: Sparse-Constrained OPtimization via itErative-solvers
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Updated
Jun 22, 2024 - Python
skscope: Sparse-Constrained OPtimization via itErative-solvers
A collection of Benchmark functions for numerical optimization problems
Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks.
Simulation code for "Achievable Rate Maximization for Underlay Spectrum Sharing MIMO System with Intelligent Reflecting Surface," by V. Kumar, M. F. Flanagan, R. Zhang, and L. -N. Tran, IEEE Wireless Communications Letters, 2022, doi: 10.1109/LWC.2022.3180988.
Codes for the paper "Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex"
Simulation code for "On the Secrecy Rate under Statistical QoS Provisioning for RIS-assisted MISO Wiretap Channel," by V. Kumar, M. F. Flanagan, D. W. Kwan Ng, and L. -N. Tran, IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685957.
Pyoneer is a Python 3 package for the continuous recovery of non-bandlimited periodic signals with finite rates of innovation (e.g. Dirac streams) from generalised measurements.
New Matrix Factorization Algorithms based on Bregman Proximal Gradient: BPG-MF, CoCaIn BPG-MF, BPG-MF-WB
Semi-blind deconvolution for fMRI (BOLD signal)
Fast Inertial Algorithm for Phase Retrieval
Information network edge representation learning using edge-to-vertex dual graphs (a.k.a line graph). In addition to that, an optimisation problem is solved efficiently to generate the edge embeddings.
CoCaIn BPG escapes Spurious Stationary Points
Sequential Convex Optimization for TAMP problems with multiple backend solvers!
This repository contains the work done as part of my B.Tech Project
In compressed decentralized optimization settings, there are benefits to having multiple gossip steps between subsequent gradient iterations, even when the cost of doing so is appropriately accounted for e.g. by means of reducing the precision of compressed information.
Contains code for RL and NES scheduling algorithms to optimize a flood control problem in a water dam
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