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Fix PDF wrap hyphens in _posts abstracts - #3
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Repair letter- space artifacts (including cross-line YAML folds) so pmlint posts --check passes on published volume correction content.
Keep short non-words hyphenated (e.g. abc-mart) while still joining ordinary line-wraps. Regenerated from gh-pages originals.
lawrennd
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Oct 6, 2026
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With edits to spotted maths problems.
| shot sampling vari- ance while requiring O(log(n)) random bits for x $\in$Rn which | ||
| significantly improves on traditional methods such as fixed basis meth- ods, Hutchinson’s | ||
| of stochastic trace estimation. This approach offers a new state of the art single | ||
| shot sampling variance while requiring O(log(n)) random bits for x $\in$Rn which |
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| shot sampling variance while requiring O(log(n)) random bits for x $\in$Rn which | |
| shot sampling variance while requiring $O(\log(n))$ random bits for $x \in R^n$ which |
| for efficiently approximating the value of the partition function, as well as the | ||
| asso- ciated pseudo-marginals. More precisely, for pairwise models with n vertices | ||
| associated pseudo-marginals. More precisely, for pairwise models with n vertices | ||
| and m edges, the complexity can be reduced from O(dk) to O(k4 + kn + m), where d |
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| and m edges, the complexity can be reduced from O(dk) to O(k4 + kn + m), where d | |
| and m edges, the complexity can be reduced from $O(d^k)$ to $O(k^4 + kn + m)$, where $d |
| has recently been proposed for probabilistic clustering. Compared with existing | ||
| solutions, this algorithm is simple to implement, and has no hyperparameters to | ||
| be tuned. Building on the recent advances of FW algorithms in nonconvex optimization, | ||
| we prove an O(1/$\varepsilon$2) convergence rate to $\varepsilon$-approximate KKT |
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| we prove an O(1/$\varepsilon$2) convergence rate to $\varepsilon$-approximate KKT | |
| we prove an $O(1/\varepsilon^2)$ convergence rate to $\varepsilon$-approximate KKT |
| to solve it. An algo- rithm designer wishes to solve a linear pro- gram (LP), max | ||
| cT x s.t. Ax $\leq$b, x $\geq$0, but does not initially know some of the pa- rameters. | ||
| to solve it. An algorithm designer wishes to solve a linear program (LP), max | ||
| cT x s.t. Ax $\leq$b, x $\geq$0, but does not initially know some of the parameters. |
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| cT x s.t. Ax $\leq$b, x $\geq$0, but does not initially know some of the parameters. | |
| $\mathbf{c}^T \mathbf{x}$ s.t. $\mathbf{A}\mathbf{x} \leq \mathbf{b}$, $\mathbf{x} \geq 0$, but does not initially know some of the parameters. |
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Summary
_postsabstracts (e.g.knowl- edge, cross-line YAMLap-\n proximations).pmlint posts --fix.Test plan
pmlint posts --checkpasses locally on this volumepmlint-postsCI on this PR is green