Interior Point Methods

By N. Ploskas, N. Samaras. 2017

404 citations recorded by the source corpus.

How researchers see this paper

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The Frank-Wolfe Algorithm: A Short Introduction (2023, neutral): “This complexity renders the use of projection-based methods for many signiﬁcant constrained problems quite challenging; in some cases relaxed projections which essentially compute separating hyperplanes can be used though.”
Fast online deconvolution of calcium imaging data (2016, background): “The interior point methods scale linearly, but they cannot be warm started well [23], i.”
Distance geometry and data science (2019, methodology): “(X), 302 L. Liberti 1 3 the Interior Point Method (IPM), which often features as a “state of the art” SDP solver, has a relatively high computational complexity (Potra and Wright 2000): a “big oh” notation estimate of O(max (m, n)mn2.”
SelectiveNet: A Deep Neural Network with an Integrated Reject Option (2019, methodology): “The contributions of this paper are: • A selective loss function that optimizes a speciﬁed coverage slice using a variant of the interior point optimization method (Potra & Wright, 2000).”
Approximate dynamic programming via iterated Bellman inequalities (2015, background): “The optimization problem (14) becomes a semidefinite program (SDP), which can be efficiently solved using convex optimization methods [54, 56, 55, 57].”