Sentence examples for very small problems from inspiring English sources

But these are very small problems in a shimmering work of multiple strengths.

"Sometimes they are very small problems … maybe someone always uses the public car for personal use," said a student, Simone Cucinotte.

The children's voices are not always convincing" and "characters' voices are occasionally too polished", but "these are very small problems in a shimmering work of multiple strengths".

Innocent people get arrested and get killed by the government, and no one cares about them, so the cellphone towers are very small problems here".

Most of these studies have been performed using very small problems and have neglected to benchmark the performances of genetic algorithms (GA) with those of hill-climbers, leaving us to wonder if a GA would be the best tool for solving any size NK-landscape problem.

We show that, for very small problem sizes, design and optimization of GPU kernels require a mindset different from the one usually used when designing LAPACK algorithms for GPUs.

Given that none of the existing general-purpose MIP solvers is able to solve even very small problem instances in a reasonable time, we propose a Lagrangian decomposition approach which uses a heuristic procedure and is capable of obtaining practical and high quality solutions in reasonable times.

"And when they do, it's a very small problem".

The knee is therefore very sensitive to small problems in alignment, training, and overuse.

Moreover, although the total sample size is over 4000, cell sizes can be very small because psychiatric problems, such as ADHD, substance use, and collisions have low prevalence; small cell sizes and large CIs suggest a low level of precision, as indicated in some of the variables in Table 3.

The third term is the accumulation of the hinge loss for differently labelled examples whose perimeters are invaded by x i. SVMs [ 46, 47] let to build reliable classifiers in very small sample size problems [ 48] and even may find nonlinear decision boundaries for small training sets.