1,589 research outputs found
Neur2RO: Neural Two-Stage Robust Optimization
Robust optimization provides a mathematical framework for modeling and
solving decision-making problems under worst-case uncertainty. This work
addresses two-stage robust optimization (2RO) problems (also called adjustable
robust optimization), wherein first-stage and second-stage decisions are made
before and after uncertainty is realized, respectively. This results in a
nested min-max-min optimization problem which is extremely challenging
computationally, especially when the decisions are discrete. We propose
Neur2RO, an efficient machine learning-driven instantiation of
column-and-constraint generation (CCG), a classical iterative algorithm for
2RO. Specifically, we learn to estimate the value function of the second-stage
problem via a novel neural network architecture that is easy to optimize over
by design. Embedding our neural network into CCG yields high-quality solutions
quickly as evidenced by experiments on two 2RO benchmarks, knapsack and capital
budgeting. For knapsack, Neur2RO finds solutions that are within roughly
of the best-known values in a few seconds compared to the three hours of the
state-of-the-art exact branch-and-price algorithm; for larger and more complex
instances, Neur2RO finds even better solutions. For capital budgeting, Neur2RO
outperforms three variants of the -adaptability algorithm, particularly on
the largest instances, with a 5 to 10-fold reduction in solution time. Our code
and data are available at https://github.com/khalil-research/Neur2RO
Machine Learning for K-adaptability in Two-stage Robust Optimization
Two-stage robust optimization problems constitute one of the hardest
optimization problem classes. One of the solution approaches to this class of
problems is K-adaptability. This approach simultaneously seeks the best
partitioning of the uncertainty set of scenarios into K subsets, and optimizes
decisions corresponding to each of these subsets. In general case, it is solved
using the K-adaptability branch-and-bound algorithm, which requires exploration
of exponentially-growing solution trees. To accelerate finding high-quality
solutions in such trees, we propose a machine learning-based node selection
strategy. In particular, we construct a feature engineering scheme based on
general two-stage robust optimization insights that allows us to train our
machine learning tool on a database of resolved B&B trees, and to apply it
as-is to problems of different sizes and/or types. We experimentally show that
using our learned node selection strategy outperforms a vanilla, random node
selection strategy when tested on problems of the same type as the training
problems, also in case the K-value or the problem size differs from the
training ones
Notes on the Markowitz portfolio selection method
Portfolio Investment;management science
A concise history of analytical accounting: examining the use of mathematical notions in our discipline.
Este trabajo ofrece una sucinta revisión de los métodos de matemática analítica empleados en teneduría de libros y contabilidad durante los últimos cinco milenios. The paper offers a succinct survey of analytical-mathematical methods as employed in bookkeeping and accounting during some five millennia.Historia de la contabilidad analítica, uso de nociones matemáticas, álgebra matricial, information perspectiva, clean surplus theory, teoría matemática de la agencia. History of analytical accounting, use of mathematical notions, matrix algebra, information perspective, clean surplus theory, mathematical agency theory.
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