Error correcting codes for distributed non-linear function computationTapha, Thitiwat Communication systemoptimizationgradient descentalternating optimizationlogistic regressionloss functionmatrix-multiplicationElectrical EngineeringB SWe provide novel coded computational strategies for distributed non-linear function computation. We utilize the idea of previous studies on coded matrix-multiplication computation and extend our study to achieve optimal error in non-linear distributed logistic regression. We consider the system of P computation nodes where each node receives two one-dimension input matrices. Our computation system reduces the error from stragglers via encoding and decoding. Our experiment with a random set of the number of worker nodes, threshold, and number of inputs has yielded conclusions on the effect of parameters on the loss function. Moreover, we develop an optimization framework based on alternating minimization that enables the discovery of new codes to achieve optimal coefficients to minimize the error in non-linear functions.Viveck Ramesh Cadambe, Thesis SupervisorJulio Urbina, Thesis Honors Advisoropen_access2024-04-03T21:24:49Zhttps://honors.libraries.psu.edu/catalog/8948tft5219