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Optimization Algorithms The following algorithms are compared and implemented using ABAGAIL, as provided in the class. The Java code was built using IntelliJ IDEA. Randomized Hill Climbing (RHC) Randomized Hill Climbing locates local optima by moving towards more optimal neighbors until it reaches a peak.

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Some optimization algorithms such as Conjugate Gradient and LBFGS need to reevaluate the function multiple times, so you have to pass in a closure that allows them to recompute your model. The closure should clear the gradients, compute the loss, and return it.

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Approximation, randomized, and online algorithms; Combinatorial and stochastic optimization; E-commerce. Models and algorithms for online matching, advertising, and ride-hailing platforms; Fairness, Accountability, and Transparency in Automated Systems. Fairness in machine learning clustering and classification; Demographic fairness

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Peherstorfer, B., Drmac, Z. & Gugercin, S. Stability of discrete empirical interpolation and gappy proper orthogonal decomposition with randomized and deterministic sampling points. SIAM Journal on Scientific Computing , 2020 .

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Existing Methods For Robust Optimization: Sampling based Approaches • R. Tempo, G. Calafiore, and F. Dabbene, Randomized Algorithms for Analysis and Control of Uncertain Systems, Communications and Control Engineering Series, Springer- Verlag, London, 2013. • Uncertain Conic Optimization • Uncertain Quadratic Problems

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This code runs two different Markov decision processes and one Q-learning analysis of various grid world problems for assignment 4 of CS-7641 at Georgia Tech. Running the Code.