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Then, by applying the multi-valued version of the fixed-point theorem and the extended version of the theory of majorants, we derive a practical condition in the form of inequalities that can be solved in practice.
The high complexity of this problem makes it impossible to be solved in practice with commercial software.
Apart from the promise of performing quantum computations, however, these investigations also revealed a great deal of difficulties which still need to be solved in practice.
Then, by extending a known theory and applying it to the so-obtained linear problem, we derive design inequalities that can readily be solved in practice.
The optimization of ℓ0 norm which is non-convex quadratically constrained optimization is NP-hard and cannot be solved in practice.
In this regard, it is traditional to distinguish pre-theoretically between the class of feasibly decidable problems – i.e. those which can be solved in practice by an efficient algorithm – and the class of intractable problems – i.e. those which lack such algorithms and may thus be regarded as intrinsically difficult to decide (despite possibly being decidable in principle).
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In this seminar, students will engage in research on how intractable problems are solved in practice.
Integer Linear Programming (ILP) can be seen as the archetypical problem for NP-complete optimization problems, and a wide range of problems in artificial intelligence are solved in practice via a translation to ILP.
The structure determination, variable selection and distributed computation are solved in practice.
The random-utility-based multiregional input output (RUBMRIO) model has been solved in practice by iteratively applying a set of equations.
This can be solved in clinical practice using good clinical judgement, but in epidemiological studies patients with missing baseline values are either assumed to be normal, estimated from other results or excluded.
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