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Stochastic Optimization
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Stochastic Optimization is the task of optimizing certain objective functional by generating and using stochastic random variables. Usually the Stochastic Optimization is an iterative process of generating random variables that progressively finds out the minima or the maxima of the objective functional. Stochastic Optimization is usually applied in the non-convex functional spaces where the usual deterministic optimization such as linear or quadratic programming or their variants cannot be used.
Source: ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/uwjunqi/Subrank
공식 코드Unsupervised Submodular Rank Aggregation on Score-based Permutations
https://github.com/vfcosta/qd-coegan
공식 코드https://github.com/vgupta1/JS_SAA
공식 코드https://github.com/viggi999999/evoalgocode
공식 코드https://github.com/vijayiitkgp/binary_search_gradient_optimization
공식 코드Binary Search and First Order Gradient Based Method for Stochastic Optimization
https://github.com/vincentvdschaft/off-grid-ultrasound
공식 코드https://github.com/vineeths96/Gradient-Compression
공식 코드Unbiased Single-scale and Multi-scale Quantizers for Distributed Optimization
https://github.com/vineeths96/heterogeneous-systems
공식 코드Distributed Optimization using Heterogeneous Compute Systems
https://github.com/virtuosoresearch/noise-stability-optimization
공식 코드Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization Approach
https://github.com/vis-sustech/paretotracker
공식 코드https://github.com/vishnupandi/FedNDL3
공식 코드On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing
https://github.com/vojha-code/saofeas
공식 코드https://github.com/vvipgupta/OverSketchedNewton
공식 코드OverSketched Newton: Fast Convex Optimization for Serverless Systems
https://github.com/wangronin/EMO21
공식 코드On Statistical Analysis of MOEAs with Multiple Performance Indicators
https://github.com/wangronin/hypervolumederivatives
공식 코드https://github.com/weibenzhang07/ng-hvi
공식 코드Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models
https://github.com/wenhua-li/commea
공식 코드Coevolutionary Framework for Generalized Multimodal Multi-objective Optimization
https://github.com/wenhua-li/comparativestudyofmmop
공식 코드Multimodal Multi-objective Optimization: Comparative Study of the State-of-the-Art
https://github.com/wgraysonp/rmiso
공식 코드Stochastic optimization with arbitrary recurrent data sampling
https://github.com/wilderlavington/target-based-surrogates-for-stochastic-optimization
공식 코드https://github.com/writingpeter/coil_gecco22
공식 코드COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutions
https://github.com/wuxingyu-ai/llm4ec
공식 코드Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
https://github.com/xidongwu/d-auprc
공식 코드Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining
https://github.com/xidulu/jointcv
공식 코드Joint control variate for faster black-box variational inference
https://github.com/xminghsueh/sas-ckt
공식 코드Surrogate-Assisted Search with Competitive Knowledge Transfer for Expensive Optimization
https://github.com/xtractopen/meganet.m
공식 코드slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks
https://github.com/xuchen0427/fairsync
공식 코드FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
https://github.com/xunzhaoyu/experience-based-saea
공식 코드Experience-Based Evolutionary Algorithms for Expensive Optimization
https://github.com/xwuShirley/pytorch/blob/master/torch/optim/adagradnorm.py
공식 코드AdaGrad stepsizes: Sharp convergence over nonconvex landscapes
https://github.com/xylee95/md-pgt
공식 코드MDPGT: Momentum-based Decentralized Policy Gradient Tracking
https://github.com/xzhang2523/libmoon
공식 코드LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
https://github.com/y1fanhe/cec2024
공식 코드Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming
https://github.com/yandex-research/btard
공식 코드https://github.com/yandex-research/moshpit-sgd
공식 코드Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
https://github.com/yaroslavkliukin/Clipped-AdaGrad-and-Adam
공식 코드Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.