cross vs good-humored: What's the difference?

Published:
Views: 186 views

Which is correct: cross or good-humored

How to spell cross?

cross
Correct Spelling
good-humored
Correct Spelling
crossverb

meet at a point

crossverb

to cover or extend over an area or time period

crossadjective

extending or lying across; in a crosswise direction; at right angles to the long axis

crossverb

travel across or pass over

crossadjective

annoyed and irritable

crossnoun

any affliction that causes great suffering

crossnoun

a marking that consists of lines that cross each other

crossnoun

(genetics) the act of mixing different species or varieties of animals or plants and thus to produce hybrids

crossverb

hinder or prevent (the efforts, plans, or desires) of

crossnoun

(genetics) an organism that is the offspring of genetically dissimilar parents or stock; especially offspring produced by breeding plants or animals of different varieties or breeds or species

crossverb

breed animals or plants using parents of different races and varieties

crossnoun

a representation of the structure on which Jesus was crucified; used as an emblem of Christianity or in heraldry

crossnoun

a wooden structure consisting of an upright post with a transverse piece

crossverb

fold so as to resemble a cross

crossverb

meet and pass

crossverb

trace a line through or across

good-humoredadjective

disposed to please

cross Illustrations

Popular Comparisons
abs vs asf
price vs fee
price vs fee
hell0o vs lknds
rude vs forward
tranquil vs nervous
pretty vs handsome
ninja vs pirate
discourse vs discord
popular vs popularest
determined vs devoted
cheo vs i
pretty vs handsome
compilation vs collection
joi vs poop
gaslight vs scapegoat
bog vs marsh
combat vs battle
aisles vs isle
ninja vs pirate

Latest Comparisons

alan vs tia
alan vs tia
alan vs tia
alan vs tia
dsss vs sss
stamina vs endurance
coinage vs coin
coinage vs coin
91,523,116 vs word
charming vs clever
charming vs clever
idea vs actuality
sentence vs line
integrity vs integrity
hama vs gfhg
eight vs nine
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.
put themselves vs place themselves
pene vs tetas
pene vs tetas

Trending Comparisons
eight vs nine
idea vs actuality
sentence vs line
hama vs gfhg
integrity vs integrity
charming vs clever
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.
coinage vs coin
91,523,116 vs word
coinage vs coin
charming vs clever
stamina vs endurance
dsss vs sss
alan vs tia
alan vs tia
alan vs tia
alan vs tia