optimization Vector Product

GitHub Repo https://github.com/Thaneshwar-sahu/Price-Feature_Vector_Analysis_for_Beverage_Mug_Product_Optimization

Thaneshwar-sahu/Price-Feature_Vector_Analysis_for_Beverage_Mug_Product_Optimization

This project aims to optimize the price-feature vector for a beverage mug line, identifying the best combination of price and features to enhance market competitiveness. The goal is to determine which features and price points attract consumers, helping to create a product that balances consumer preferences with market trends.

GitHub Repo https://github.com/Omimacgithub/spmv

Omimacgithub/spmv

Sparse Matrix-Vector product using different storage formats and gcc optimization tricks

GitHub Repo https://github.com/carlosdoalgarve/DotProduct

carlosdoalgarve/DotProduct

Parallel optimization of the vectorial dot product

GitHub Repo https://github.com/Paniz-Peiravani/Optimization-of-Dot-Product

Paniz-Peiravani/Optimization-of-Dot-Product

Optimization of dot product computation of two vectors using vector instructions.

GitHub Repo https://github.com/mehatabnabi/Product-Optimization

mehatabnabi/Product-Optimization

Identifying the best price-feature-vector

GitHub Repo https://github.com/Ankurx7/NLP_basedSearchPlatform_v2

Ankurx7/NLP_basedSearchPlatform_v2

NLP-powered search platform leveraging WinkNLP, cosine similarity vectors, and intelligent semantic analysis. Features multi-modal search architecture with Elasticsearch and MongoDB integration, fuzzy matching with Fuse.js, and sophisticated re-ranking systems for context-aware product discovery and superior search relevance optimization.

GitHub Repo https://github.com/SENATOROVAI/singular-value-decomposition-svd-solver-course

SENATOROVAI/singular-value-decomposition-svd-solver-course

Singular Value Decomposition (SVD) is a fundamental linear algebra technique that factorizes any into the product of three matrices: are orthogonal matrices containing left and right singular vectors, while sigma is a diagonal matrix of non-negative singular values. It is essential for data reduction, noise removal, and matrix approximation.Solver

GitHub Repo https://github.com/segfaultscribe/SIMD-Dot-product-Optimization