2,002 research outputs found
The Quest for Stability: The Macro View
On September 3-4, 2009 SUERF and Utrecht University School of Economicsorganized the Colloquium "The Quest for Stability" in Utrecht, the Netherlands. The papers included in this SUERF Study are based on contributions to the Colloquium
Deep Policy Dynamic Programming for Vehicle Routing Problems
Routing problems are a class of combinatorial problems with many practical
applications. Recently, end-to-end deep learning methods have been proposed to
learn approximate solution heuristics for such problems. In contrast, classical
dynamic programming (DP) algorithms guarantee optimal solutions, but scale
badly with the problem size. We propose Deep Policy Dynamic Programming (DPDP),
which aims to combine the strengths of learned neural heuristics with those of
DP algorithms. DPDP prioritizes and restricts the DP state space using a policy
derived from a deep neural network, which is trained to predict edges from
example solutions. We evaluate our framework on the travelling salesman problem
(TSP), the vehicle routing problem (VRP) and TSP with time windows (TSPTW) and
show that the neural policy improves the performance of (restricted) DP
algorithms, making them competitive to strong alternatives such as LKH, while
also outperforming most other 'neural approaches' for solving TSPs, VRPs and
TSPTWs with 100 nodes.Comment: 21 page
The Quest for Stability: the macro view
On September 3-4, 2009 SUERF and Utrecht University School of Economicsorganized the Colloquium "The Quest for Stability" in Utrecht, the Netherlands. The papers included in this SUERF Study are based on contributions to the Colloquium.asset prices, bubbles, financial institutions, global recession, interest rates, liquidity, monetary policy, regulation, stability, supervision.
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