Results 181 to 190 of about 1,352 (208)

Submodularity and adaptation

2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU), 2007
Summary form only given. Convexity is a property of real-valued functions that enable their efficient optimization. Convex optimization moreover is a problem onto which an amazing variety of practical problems can be cast. Having strong analogs to convexity, submodularity is a property of functions on discrete sets that allows their optimization to be ...
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Directed submodularity, ditroids and directed submodular flows

Mathematical Programming, 1988
Set relations and operations such as inclusion, union and intersection are generalized to directed subsets whose elements are distinguished between forward and backward elements. The concepts of submodular functions, matroids and polymatroidal network flows are extended to the concepts of directed submodular functions, ditroids and directed submodular ...
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The median partition and submodularity

Applied Mathematics and Computation, 2021
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Submodular Risk Allocation

Management Science, 2017
We analyze the optimal allocation of trades to portfolios when the cost associated with an allocation is proportional to each portfolio’s risk. Our investigation is motivated by changes in the over-the-counter derivatives markets, under which some contracts may be traded bilaterally or through central counterparties, splitting a set of trades into two
Samim Ghamami, Paul Glasserman
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On submodular function minimization

Combinatorica, 1985
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Decomposition of submodular functions

Combinatorica, 1983
A decomposition theory for submodular functions is described. Any such function is shown to have a unique decomposition consisting of indecomposable functions and certain highly decomposable functions, and the latter are completely characterized. Applications include decompositions of hypergraphs based on edge and vertex connectivity, the decomposition
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Submodular functions and convexity

1983
In “continuous” optimization convex functions play a central role. Besides elementary tools like differentiation, various methods for finding the minimum of a convex function constitute the main body of nonlinear optimization. But even linear programming may be viewed as the optimization of very special (linear) objective functions over very special ...
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Submodular function minimization

Mathematical Programming, 2007
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Stochastic Submodular Maximization

2008
We study stochastic submodular maximization problem with respect to a cardinality constraint. Our model can capture the effect of uncertainty in different problems, such as cascade effects in social networks, capital budgeting, sensor placement, etc. We study non-adaptive and adaptive policies and give optimal constant approximation algorithms for both
Arash Asadpour, Hamid Nazerzadeh, Amin Saberi   +2 more
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A note on the implications of approximate submodularity in discrete optimization

Optimization Letters, 2022
Andrew Schaefer, Temitayo Ajayi, Taewoo Lee   +2 more
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