Definable nonconvex parametric optimization problems admit an adjoint state formula under a qualification condition, selecting a conservative field for the value function without smoothness or uniqueness assumptions.
Sensitivity analysis for parametric nonlinear programming: A tutorial
2 Pith papers cite this work. Polarity classification is still indexing.
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A co-optimization framework for power system capacity and demand-shaping policies that uses differentiable scenario generation from generative machine learning models.
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The adjoint state method for parametric definable optimization without smoothness or uniqueness
Definable nonconvex parametric optimization problems admit an adjoint state formula under a qualification condition, selecting a conservative field for the value function without smoothness or uniqueness assumptions.
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Integrated Investment and Policy Planning for Power Systems via Differentiable Scenario Generation
A co-optimization framework for power system capacity and demand-shaping policies that uses differentiable scenario generation from generative machine learning models.