A TensorFlow gradient descent framework optimizes portfolios over multiple objectives and constraints, matching exact solvers in simple cases and flexibly handling non-convex multi-objective problems.
Lower Order Terms for Expected Value of Traces of Frobenius of a Family of Cyclic Covers of $\mathbb{P}^1_{\mathbb{F}_q}$ and One-Level Densities
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abstract
We consider the expected value of $\mbox{Tr}(\Theta_C^n)$ where $C$ runs over a thin family of $r$-cyclic covers of $\mathbb{P}^1_{\mathbb{F}_q}$ for any $r$. We obtain many lower order terms dependent on the divisors of $r$. We use these results to calculate the one-level density of the family and hypothesize a refined one-level density result.
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2025 1verdicts
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Multi-objective Portfolio Optimization Via Gradient Descent
A TensorFlow gradient descent framework optimizes portfolios over multiple objectives and constraints, matching exact solvers in simple cases and flexibly handling non-convex multi-objective problems.