Search results
Results from the WOW.Com Content Network
First-fit-decreasing (FFD) is an algorithm for bin packing.Its input is a list of items of different sizes. Its output is a packing - a partition of the items into bins of fixed capacity, such that the sum of sizes of items in each bin is at most the capacity.
First-fit (FF) is an online algorithm for bin packing.Its input is a list of items of different sizes. Its output is a packing - a partition of the items into bins of fixed capacity, such that the sum of sizes of items in each bin is at most the capacity.
A sphygmomanometer (/ ˌ s f ɪ ɡ m oʊ m ə ˈ n ɒ m ɪ t ə r / SFIG-moh-mə-NO-mi-tər), also known as a blood pressure monitor, or blood pressure gauge, is a device used to measure blood pressure, composed of an inflatable cuff to collapse and then release the artery under the cuff in a controlled manner, [1] and a mercury or aneroid manometer to measure the pressure.
A minimum systolic value can be roughly estimated by palpation, most often used in emergency situations, but should be used with caution. [10] It has been estimated that, using 50% percentiles, carotid, femoral and radial pulses are present in patients with a systolic blood pressure > 70 mmHg, carotid and femoral pulses alone in patients with systolic blood pressure of > 50 mmHg, and only a ...
The following is the skeleton of a generic branch and bound algorithm for minimizing an arbitrary objective function f. [3] To obtain an actual algorithm from this, one requires a bounding function bound, that computes lower bounds of f on nodes of the search tree, as well as a problem-specific branching rule.
In the theory of optimal binary search trees, the interleave lower bound is a lower bound on the number of operations required by a Binary Search Tree (BST) to execute a given sequence of accesses. Several variants of this lower bound have been proven. [1] [2] [3] This article is based on a variation of the first Wilber's bound. [4]
The following Python code implements the Euler–Maruyama method and uses it to solve the Ornstein–Uhlenbeck process defined by d Y t = θ ⋅ ( μ − Y t ) d t + σ d W t {\displaystyle dY_{t}=\theta \cdot (\mu -Y_{t})\,{\mathrm {d} }t+\sigma \,{\mathrm {d} }W_{t}}
An upper bound for a decision-tree model was given by Meyer auf der Heide [17] who showed that for every n there exists an O(n 4)-deep linear decision tree that solves the subset-sum problem with n items. Note that this does not imply any upper bound for an algorithm that should solve the problem for any given n.