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Found 8 papers, 3 papers with code
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
Graph neural networks (GNNs) have been widely used to predict properties and heuristics of mixed-integer linear programs (MILPs) and hence accelerate MILP solvers.
DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee
Mixed-integer linear programming (MILP) stands as a notable NP-hard problem pivotal to numerous crucial industrial applications.
On Representing Mixed-Integer Linear Programs by Graph Neural Networks
While Mixed-integer linear programming (MILP) is NP-hard in general, practical MILP has received roughly 100--fold speedup in the past twenty years.
On Representing Linear Programs by Graph Neural Networks
In particular, the graph neural network (GNN) is considered a suitable ML model for optimization problems whose variables and constraints are permutation--invariant, for example, the linear program (LP).
Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses
In this paper, we study algorithms for dynamically identifying a large number of products (i. e., SKUs) with top customer purchase probabilities on the fly, from an ocean of potential products to offer on retailers' ultra-fast delivery platforms.
The Lingering of Gradients: Theory and Applications
Classically, the time complexity of a first-order method is estimated by its number of gradient computations.
The Lingering of Gradients: How to Reuse Gradients Over Time
Classically, the time complexity of a first-order method is estimated by its number of gradient computations.
Inventory Balancing with Online Learning
We overcome both the challenges of model uncertainty and customer heterogeneity by judiciously synthesizing two algorithmic frameworks from the literature: inventory balancing, which "reserves" a portion of each resource for high-reward customer types which could later arrive, and online learning, which shows how to "explore" the resource consumption distributions of each customer type under different actions.
