bottom vs origin: What's the difference?

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Which is correct: bottom or Origin

How to spell bottom?

bottom
Correct Spelling
Origin
Correct Spelling
bottomnoun

low-lying alluvial land near a river

bottomnoun

a cargo ship

bottomnoun

the second half of an inning; while the home team is at bat

bottomnoun

a depression forming the ground under a body of water

bottomnoun

the lower side of anything

bottomnoun

the fleshy part of the human body that you sit on

bottomverb

come to understand

bottomverb

provide with a bottom or a seat

bottomadjective

situated at the bottom or lowest position

bottomverb

strike the ground, as with a ship's bottom

bottomnoun

the lowest part of anything

bottomadjective

the lowest rank

Originnoun

the descendants of one individual

Originnoun

the place where something begins, where it springs into being

Originnoun

properties attributable to your ancestry

Originnoun

an event that is a beginning; a first part or stage of subsequent events

Originnoun

the point of intersection of coordinate axes; where the values of the coordinates are all zero

Originnoun

the source of something's existence or from which it derives or is derived

bottom Illustrations

Origin Illustrations

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heuristic algorithms can compute an answer to the problem much more quickly than exact ones, however the likelihood that it is the optimal one can vary wildly between solutions. though for most use cases, it isn’t too important. as shown in \cite{rego2011427}, most modern heuristic methods can achieve a solution accuracy of between 2-3\% most of the time. this is however, still at the cost of long computation time. vs heuristic algorithms are approximate methods that aim to find near-optimal solutions to complex problems, like the traveling salesman problem (tsp), in a more computationally efficient manner compared to exact methods. although heuristic algorithms do not guarantee an optimal solution, they can often provide high-quality solutions that are sufficient for most practical applications. in the context of the tsp, heuristic algorithms have been widely studied and employed due to their effectiveness in solving large instances of the problem, where exact methods become computationally intractable. some of the most well-known heuristic algorithms for tsp include: nearest neighbor algorithm: this simple greedy algorithm starts at an initial node and repeatedly selects the closest unvisited node as the next node in the tour until all nodes have been visited. although this method is fast, it can sometimes produce suboptimal solutions. greedy insertion: this approach involves iteratively inserting nodes into an existing tour by selecting the node and insertion position that result in the smallest increase in tour length. variants of this algorithm, such as cheapest insertion and farthest insertion, use different criteria for node selection and insertion. 2-opt local search: this technique focuses on improving an existing tour by iteratively swapping pairs of edges to reduce the overall tour length. the process continues until no further improvements can be made. the 2-opt algorithm can be combined with other heuristics, like the nearest neighbor or greedy insertion, to further enhance their performance. metaheuristics: advanced heuristic algorithms, known as metaheuristics, have also been applied to the tsp. these methods, such as simulated annealing, genetic algorithms, ant colony optimization, and particle swarm optimization, employ higher-level strategies to guide the search process and avoid getting stuck in local optima. as reported in rego et al. (2011), modern heuristic methods can achieve solution accuracies of between 2-3% most of the time, making them suitable for a wide range of applications. however, it is important to note that the computational efficiency of heuristic algorithms may still be an issue for very large problem instances or when tight deadlines are imposed. in conclusion, heuristic algorithms for the tsp offer a trade-off between solution quality and computational efficiency, making them a valuable tool in practical settings where exact methods are not feasible. by carefully selecting and tuning heuristic methods, it is possible to obtain near-optimal solutions in a relatively short amount of time, satisfying the requirements of many real-world applications.
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heuristic algorithms can compute an answer to the problem much more quickly than exact ones, however the likelihood that it is the optimal one can vary wildly between solutions. though for most use cases, it isn’t too important. as shown in \cite{rego2011427}, most modern heuristic methods can achieve a solution accuracy of between 2-3\% most of the time. this is however, still at the cost of long computation time. vs heuristic algorithms are approximate methods that aim to find near-optimal solutions to complex problems, like the traveling salesman problem (tsp), in a more computationally efficient manner compared to exact methods. although heuristic algorithms do not guarantee an optimal solution, they can often provide high-quality solutions that are sufficient for most practical applications. in the context of the tsp, heuristic algorithms have been widely studied and employed due to their effectiveness in solving large instances of the problem, where exact methods become computationally intractable. some of the most well-known heuristic algorithms for tsp include: nearest neighbor algorithm: this simple greedy algorithm starts at an initial node and repeatedly selects the closest unvisited node as the next node in the tour until all nodes have been visited. although this method is fast, it can sometimes produce suboptimal solutions. greedy insertion: this approach involves iteratively inserting nodes into an existing tour by selecting the node and insertion position that result in the smallest increase in tour length. variants of this algorithm, such as cheapest insertion and farthest insertion, use different criteria for node selection and insertion. 2-opt local search: this technique focuses on improving an existing tour by iteratively swapping pairs of edges to reduce the overall tour length. the process continues until no further improvements can be made. the 2-opt algorithm can be combined with other heuristics, like the nearest neighbor or greedy insertion, to further enhance their performance. metaheuristics: advanced heuristic algorithms, known as metaheuristics, have also been applied to the tsp. these methods, such as simulated annealing, genetic algorithms, ant colony optimization, and particle swarm optimization, employ higher-level strategies to guide the search process and avoid getting stuck in local optima. as reported in rego et al. (2011), modern heuristic methods can achieve solution accuracies of between 2-3% most of the time, making them suitable for a wide range of applications. however, it is important to note that the computational efficiency of heuristic algorithms may still be an issue for very large problem instances or when tight deadlines are imposed. in conclusion, heuristic algorithms for the tsp offer a trade-off between solution quality and computational efficiency, making them a valuable tool in practical settings where exact methods are not feasible. by carefully selecting and tuning heuristic methods, it is possible to obtain near-optimal solutions in a relatively short amount of time, satisfying the requirements of many real-world applications.
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