531 skills found · Page 4 of 18
mkhraijah / PowerModelsADA.jlA package for solving optimal power flow problems using distributed algorithms.
yzf072 / SpaTrackAn optimal transport-based algorithm for inferring cell trajectories of spatial transcriptomics.
Hazrat-Ali9 / Data Structures And Algorithms Practice📔 Data 📕 Structures 📗 and 📘 Algorithms 📚 Practice is 📙 a comprehensive 📓 coding 📟 problems 💰clean 🧲 implementations 🪣 and 🏪 optimal ⚽ solutions ⚾ using the 🥎 most 🕌 important 🏀 Data 🏭 Structures 🏈 and Algorithms 🎳 preparing 🎮 coding 🚋 interviews ✈ competitive 🚀 programming 🛸 mastering 🛬 problem 🚁 solving this ⛴ repo has 🚤
Xenophyophoreee / NSGA2 Multi Target Scheduling Of ReservoirsThe NSGA2 algorithm is used to solve the multi-objective optimization problem, and the optimal scheduling objectives include minimizing ecological water demand and maximizing power generation efficiency.
KULeuvenNeuromechanics / MuscleRedundancySolverAn algorithm to estimate muscle tendon properties and/or compute muscle coordination by tracking experimental data with a musculoskeletal model assuming optimal control to solve for the muscle redundancy.
WheelChan / Multi Agent Path FindingGoal of project is to write programs that will allow multiple agents to find optimal paths from their start location to their goal location such that all agents are able to move simultaneously. Various algorithms will be explored such as Prioritized Planning, Conflict-Based Search (CBS), A* Search. Additionally, a paper that describes Safe Interval Path Planning (SIPP) provided the inspiration for writing of SIPP versions of Prioritized Planning and CBS.
tstran155 / Optimization Of Building Energy ConsumptionThis repo demonstrates how to build a surrogate (proxy) model by multivariate regressing building energy consumption data (univariate and multivariate) and use (1) Bayesian framework, (2) Pyomo package, (3) Genetic algorithm with local search, and (4) Pymoo package to find optimum design parameters and minimum energy consumption.
russwong89 / Sharpness Detection AutofocusA contrast-based autofocusing algorithm using sample images to determine the optimal focal distance for DSLR cameras
ebarnett2 / BatotpA bisection algorithm for time-optimal trajectory planning along fully specified paths
decadenza / DirectStereoRectification"Rectifying Homographies for Stereo Vision: Analytical Solution for Minimal Distortion": algorithm to compute the optimal rectifying homographies that minimise perspective distortion.
JasonAltschuler / OptimalTransportNIPS17Code for NIPS 2017 spotlight paper: "Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration" by Jason Altschuler, Jonathan Weed, and Philippe Rigollet. Full paper available at: https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.
StavrosOrf / EV GNNImproving scalability of RL algorithms using GNNs: A case study in optimal EV charging.
theoad / Dot DmaxOfficial Pytorch implementation of "Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration"
jibsen / Blz4Example of LZ4 compression with optimal parsing using BriefLZ algorithms
Rainyfish / 2E VRP ABCIn the process of solving the 2E-VRP problem, the large-scale destruction and repair algorithm is used to ensure that the algorithm does not fall into the local optimal solution. The process of the initialization process uses the greedy strategy to cluster the customers. The large-scale destruction process is to randomly remove the customer nodes on the satellite into the customer pool. The repair process is based on the reciprocal of the customer's distance to each customer in the customer pool. Gambling Select the satellite to which the customer belongs and engage in greedy insertion. For the second layer of path planning, you need to use multiple search operators, such as random sequence reversal exchange operator, crossover operator, damage and repair operator, and crossover operator variants, etc. to improve the artificial bee group algorithm Of the local search ability. To ensure that the global optimal situation can be found, the neighborhood of large-scale search. The improved artificial bee colony algorithm incorporates the idea of simulated annealing and improves the global optimization ability of artificial bee colony algorithm. For the artificial bee colony algorithm, the combination of global optimization ability and local optimization ability improves the possibility that the algorithm can find a better solution than the existing method. Multi - operator artificial bee colony algorithm, which extends the search range of the food source 's neighborhood, and more possibilities to find the global optimal solution. The experimental results show that the algorithm can get better path planning results
nessry / Hybrid BeamformingThis example introduces the basic concept of hybrid beamforming and shows how to split the precoding and combining weights using orthogonal matching pursuit algorithm. It shows that hybrid beamforming can closely match the performance offered by optimal digital weights. This program simulates a 64 x 16 MIMO hybrid beamforming system, with a 64-element square array with 4 RF chains on the transmitter side and a 16-element square array with 4 RF chains on the receiver side. Each antenna element can be connected to one or more TR modules.
dipankarsk / Feature Selection HybridIntrusion Detection is a technique to identify the abnormal behavior of system due to attack. The unusual behavior of the environment is then identified and steps are taken and methods are formed to classify and recognize attacks. Data set containing a number of records sometimes may decrease the classifiers performance due to redundancy of data. The other problems may include memory requirements and processing power so we need to either reduce the number of data or the number of records. Feature Selection techniques are used to reduce the vertical largeness of data set. This project makes a comparative study of Particle Swarm Optimization, Genetic Algorithm and a hybrid of the two where we see that PSO being simpler swarm algorithm works for feature selection problems but since it is problem dependent and more over its stochastic approach makes it less efficient in terms of error reduction compared to GA. In standard PSO, the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on sub optimal solutions that are not even guaranteed to be local optimum. A further drawback is that stochastic approaches have problem-dependent performance. This dependency usually results from the parameter settings in each algorithm. The different parameter settings for a stochastic search algorithm result in high performance variances. In this project the modification strategies are proposed in PSO using GA. Experimental results show that GA performs better than PSO for the feature selection in terms of error reduction problems whereas hybrid outperforms both the model in terms of error reduction.
CWI-EvolutionaryIntelligence / GOMEALibrary for optimization with the model-based evolutionary algorithm GOMEA (Gene-pool Optimal Mixing Evolutionary Algorithm)
open2333 / PyOLHSpython code for Optimal LatinHyperCube Sampling algorithm
ParCIS / Ok TopkOk-Topk is a scheme for distributed training with sparse gradients. Ok-Topk integrates a novel sparse allreduce algorithm (less than 6k communication volume which is asymptotically optimal) with the decentralized parallel Stochastic Gradient Descent (SGD) optimizer, and its convergence is proved theoretically and empirically.
