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Simulated Annealing can be used to solve combinatorial problems. Here it is applied to the [[travelling salesman problem to minimize the length of a route that connects all 125 points.]]

Simulated Annealing can be used to solve combinatorial problems. Here it is applied to the [[travelling salesman problem to minimize the length of a route that connects all 125 points.]]

模拟退火可以用来解决组合问题。这里我们将它应用到[[旅行商问题-最小化连接所有125个点的路径长度]。]

模拟退火可以用来解决组合问题。这里我们将它应用到旅行商问题-最小化连接所有125个点的路径长度。

Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. It is often used when the search space is discrete (e.g., the traveling salesman problem). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to alternatives such as gradient descent.

Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. It is often used when the search space is discrete (e.g., the traveling salesman problem). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to alternatives such as gradient descent.

Simulated annealing (SA)模拟退火是一种用于逼近给定函数全局最优值的概率方法。具体来说，它是在大搜索空间中针对优化问题近似全局优化提出的一种元启发法。当搜索空间是离散的(例如，旅行商问题)时，经常使用它。对于那些在一定时间内找到一个近似的全局最优解比找到一个精确的局部最优解更重要的问题，模拟退火可能比诸如梯度下降等替代方法更可取。

Simulated annealing (SA)模拟退火是一种用于逼近给定函数全局最优值的概率方法。具体来说，它是在大搜索空间中针对优化问题近似全局优化提出的一种元启发法。当搜索空间是离散的(例如旅行商问题)时，经常使用它。对于那些在一定时间内找到一个近似的全局最优解比找到一个精确的局部最优解更重要的问题，模拟退火可能比诸如梯度下降等替代方法更可取。