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Found 8 papers, 2 papers with code
Decoupling Learning and Decision-Making: Breaking the $\mathcal{O}(\sqrt{T})$ Barrier in Online Resource Allocation with First-Order Methods
More importantly, for the first time, we show that first-order methods can attain regret $\mathcal{O}(T^{1/3})$ with this new framework.
A Homogenization Approach for Gradient-Dominated Stochastic Optimization
In this paper, we propose the stochastic homogeneous second-order descent method (SHSODM) for stochastic functions enjoying gradient dominance property based on a recently proposed homogenization approach.
Pre-trained Mixed Integer Optimization through Multi-variable Cardinality Branching
We propose a new method to accelerate online Mixed Integer Optimization with Pre-trained machine learning models (PreMIO).
Stochastic Dimension-reduced Second-order Methods for Policy Optimization
In this paper, we propose several new stochastic second-order algorithms for policy optimization that only require gradient and Hessian-vector product in each iteration, making them computationally efficient and comparable to policy gradient methods.
DRSOM: A Dimension Reduced Second-Order Method
In this paper, we propose a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization.
From an Interior Point to a Corner Point: Smart Crossover
The crossover method, which aims at deriving an optimal extreme point from a suboptimal solution (the output of a starting method such as interior-point methods or first-order methods), is crucial in this process.
Optimization and Control 90C05
Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing
In this paper, using the problem of demand function prediction in dynamic pricing as the motivating example, we study the problem of constructing accurate confidence intervals for the demand function.
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem
Computing the Wasserstein barycenter of a set of probability measures under the optimal transport metric can quickly become prohibitive for traditional second-order algorithms, such as interior-point methods, as the support size of the measures increases.
