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Found 7 papers, 3 papers with code
OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models
Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare.
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.
Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity
This paper considers stochastic weakly convex optimization without the standard Lipschitz continuity assumption.
OptiMUS: Optimization Modeling Using MIP Solvers and large language models
Optimization problems are pervasive across various sectors, from manufacturing and distribution to healthcare.
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).
Optimal Diagonal Preconditioning
Moreover, our implementation of customized solvers, combined with a random row/column sampling step, can find near-optimal diagonal preconditioners for matrices up to size 200, 000 in reasonable time, demonstrating their practical appeal.
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization
Second, motivated by the success of momentum stochastic gradient descent, we propose a new stochastic extrapolated model-based method, greatly extending the classic Polyak momentum technique to a wider class of stochastic algorithms for weakly convex optimization.
