Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Complexity certifications of first order inexact Lagrangian methods for
  general convex programming   In this chapter we derive computational complexity certifications of first
order inexact dual methods for solving general smooth constrained convex
problems which can arise in real-time applications, such as model predictive
control. When it is difficult to project on the primal constraint set described
by a collection of general convex functions, we use the Lagrangian relaxation
to handle the complicated constraints and then, we apply dual (fast) gradient
algorithms based on inexact dual gradient information for solving the
corresponding dual problem. The iteration complexity analysis is based on two
types of approximate primal solutions: the primal last iterate and an average
of primal iterates. We provide sublinear computational complexity estimates on
the primal suboptimality and constraint (feasibility) violation of the
generated approximate primal solutions. In the final part of the chapter, we
present an open-source quadratic optimization solver, referred to as DuQuad,
for convex quadratic programs and for evaluation of its behaviour. The solver
contains the C-language implementations of the analyzed algorithms.

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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: cs
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The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex