Example heuristics and their encoding in MetaOpt (sub-figures (b) and (c)). Heuristic in sub-figure (b) forces the demands less than a threshold to be pinned and then solves a flow maximization problem, heuristic in sub-figure (c) assigns the first bin that can fit the ball.

Contexts in source publication

Context 1

... it routes through the network without exceeding the network capacity. Operators use DP to reduce the size of the optimization problem they solve. DP first filters all demands below a pre-defined threshold and routes them through (pins them to) their shortest path. It then routes the remaining demands optimally using the available capacity (see Fig. ...

Context 2

... authors modeled DP directly as an optimization problem. They also provided a number of helper functions that allow operators to model it more easily (Fig. 1b). MetaOpt solves a bi-level optimization that produces the performance gap and demand that causes it (the flow in Fig. 1a). It is easy to see what is missing: it is up to the operator to examine the single output and find why DP underperformed. DP is amenable to such manual analysis (see [35]), but not all heuristics are. It is also ...

Context 3

... authors modeled DP directly as an optimization problem. They also provided a number of helper functions that allow operators to model it more easily (Fig. 1b). MetaOpt solves a bi-level optimization that produces the performance gap and demand that causes it (the flow in Fig. 1a). It is easy to see what is missing: it is up to the operator to examine the single output and find why DP underperformed. DP is amenable to such manual analysis (see [35]), but not all heuristics are. It is also hard for operators to extrapolate from this example adversarial input and find all other regions of the input space where DP ...

Context 4

... VBP problem is APX-hard [45]. One heuristic that solves VBP is first-fit (FF), which greedily places an incoming ball in the first bin it fits in. Fig. 1c shows how we can encode it in ...

Context 5

... DP as an example. The ideal tool would produce: Type 1. For a given topology, the adversarial input sets are of the form ∪í µí°· í µí±– where each í µí°· í µí±– ∈ R í µí±› + represents a contiguous subspace of the n-dimensional (8-dimensional in Fig. 1a for 8 demands) ...

Context 6

... all other í µí±¢í µí±£ where a portion of the path between the nodes í µí±¢ and í µí±£ intersects with the shortest path of a pinnable demand we have í µí±‘ í µí±¢í µí±£ ≥ min(C í µí±¢í µí±£ −í µí±‡ ). Here, the set C í µí±¢í µí±£ contains the capacity of all links on the path between í µí±¢ and í µí±£. The adversarial instance in our example in Fig. 1a fits this ...

Context 7

... How we model our DP example (Fig. 1a) in the DSL. and we use "pick nodes" with limited capacity that only allow a ball to be assigned to a single bin (Fig. 4b) to model FF. We prove that we can represent any linear or mixed integer problem through a small set of node behaviors (our abstraction is sufficient) in App. ...

Context 8

... it routes through the network without exceeding the network capacity. Operators use DP to reduce the size of the optimization problem they solve. DP first filters all demands below a pre-defined threshold and routes them through (pins them to) their shortest path. It then routes the remaining demands optimally using the available capacity (see Fig. ...

Context 9

... authors modeled DP directly as an optimization problem. They also provided a number of helper functions that allow operators to model it more easily (Fig. 1b). MetaOpt solves a bi-level optimization that produces the performance gap and demand that causes it (the flow in Fig. 1a). It is easy to see what is missing: it is up to the operator to examine the single output and find why DP underperformed. DP is amenable to such manual analysis (see [35]), but not all heuristics are. It is also ...

Context 10

... authors modeled DP directly as an optimization problem. They also provided a number of helper functions that allow operators to model it more easily (Fig. 1b). MetaOpt solves a bi-level optimization that produces the performance gap and demand that causes it (the flow in Fig. 1a). It is easy to see what is missing: it is up to the operator to examine the single output and find why DP underperformed. DP is amenable to such manual analysis (see [35]), but not all heuristics are. It is also hard for operators to extrapolate from this example adversarial input and find all other regions of the input space where DP ...

Context 11

... VBP problem is APX-hard [45]. One heuristic that solves VBP is first-fit (FF), which greedily places an incoming ball in the first bin it fits in. Fig. 1c shows how we can encode it in ...

Context 12

... DP as an example. The ideal tool would produce: Type 1. For a given topology, the adversarial input sets are of the form ∪í µí°· í µí±– where each í µí°· í µí±– ∈ R í µí±› + represents a contiguous subspace of the n-dimensional (8-dimensional in Fig. 1a for 8 demands) ...

Context 13

... all other í µí±¢í µí±£ where a portion of the path between the nodes í µí±¢ and í µí±£ intersects with the shortest path of a pinnable demand we have í µí±‘ í µí±¢í µí±£ ≥ min(C í µí±¢í µí±£ −í µí±‡ ). Here, the set C í µí±¢í µí±£ contains the capacity of all links on the path between í µí±¢ and í µí±£. The adversarial instance in our example in Fig. 1a fits this ...

Context 14

... How we model our DP example (Fig. 1a) in the DSL. and we use "pick nodes" with limited capacity that only allow a ball to be assigned to a single bin (Fig. 4b) to model FF. We prove that we can represent any linear or mixed integer problem through a small set of node behaviors (our abstraction is sufficient) in App. ...