The paper introduces a new algorithm and program called ADMIXTURE for fast model-based estimation of ancestry in unrelated individuals. ADMIXTURE is designed to address the issue of population stratification in genetic association studies, which can confound results. The method is based on the likelihood model used in the widely applied program *structure*, but it significantly reduces the runtime from hours to minutes. ADMIXTURE uses a block relaxation scheme with sequential quadratic programming for block updates and a quasi-Newton acceleration method to improve convergence. The authors compare ADMIXTURE's performance with other methods, including *structure*, EIGENSTRAT, and FRAPPE, showing that ADMIXTURE is almost as fast as EIGENSTRAT and provides comparable accuracy in ancestry estimation. The simulations and real-world data sets demonstrate that ADMIXTURE can be used to correct for population structure in association studies, making it a valuable tool for genetic research.The paper introduces a new algorithm and program called ADMIXTURE for fast model-based estimation of ancestry in unrelated individuals. ADMIXTURE is designed to address the issue of population stratification in genetic association studies, which can confound results. The method is based on the likelihood model used in the widely applied program *structure*, but it significantly reduces the runtime from hours to minutes. ADMIXTURE uses a block relaxation scheme with sequential quadratic programming for block updates and a quasi-Newton acceleration method to improve convergence. The authors compare ADMIXTURE's performance with other methods, including *structure*, EIGENSTRAT, and FRAPPE, showing that ADMIXTURE is almost as fast as EIGENSTRAT and provides comparable accuracy in ancestry estimation. The simulations and real-world data sets demonstrate that ADMIXTURE can be used to correct for population structure in association studies, making it a valuable tool for genetic research.