Simulated annealing

Simulated annealing Wed, 22 Jun 2022 09:13:48 GMT 2022-06-22T09:13:48Z Simulated annealing (SA) is a <a href="https://en.wikipedia.org/wiki/Probabilistic_algorithm" class="mw-redirect" title="Probabilistic algorithm" style="text-decoration-line: none; color: rgb(11, 0, 128); background: none rgb(255, 255, 255); font-family: sans-serif; font-size: 14px;">probabilistic technique</a> for approximating the <a href="https://en.wikipedia.org/wiki/Global_optimum" class="mw-redirect" title="Global optimum" style="text-decoration-line: none; color: rgb(11, 0, 128); background: none rgb(255, 255, 255); font-family: sans-serif; font-size: 14px;">global optimum</a> of a given <a href="https://en.wikipedia.org/wiki/Function_(mathematics)" title="Function (mathematics)" style="text-decoration-line: none; color: rgb(11, 0, 128); background: none rgb(255, 255, 255); font-family: sans-serif; font-size: 14px;">function</a>. Specifically, it is a <a href="https://en.wikipedia.org/wiki/Metaheuristic" title="Metaheuristic" style="text-decoration-line: none; color: rgb(11, 0, 128); background: none rgb(255, 255, 255); font-family: sans-serif; font-size: 14px;">metaheuristic</a> to approximate <a href="https://en.wikipedia.org/wiki/Global_optimization" title="Global optimization" style="text-decoration-line: none; color: rgb(11, 0, 128); background: none rgb(255, 255, 255); font-family: sans-serif; font-size: 14px;">global optimization</a> in a large <a href="https://en.wikipedia.org/wiki/Solution_space" class="mw-redirect" title="Solution space" style="text-decoration-line: none; color: rgb(11, 0, 128); background: none rgb(255, 255, 255); font-family: sans-serif; font-size: 14px;">search space</a> for an <a href="https://en.wikipedia.org/wiki/Optimization_problem" title="Optimization problem" style="text-decoration-line: none; color: rgb(11, 0, 128); background: none rgb(255, 255, 255); font-family: sans-serif; font-size: 14px;">optimization problem</a>. It is often used when the search space is discrete (e.g., the <a href="https://en.wikipedia.org/wiki/Traveling_salesman_problem" class="mw-redirect" title="Traveling salesman problem" style="text-decoration-line: none; color: rgb(11, 0, 128); background: none rgb(255, 255, 255); font-family: sans-serif; font-size: 14px;">traveling salesman problem</a>). 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 exact algorithms such as <a href="https://en.wikipedia.org/wiki/Gradient_descent" title="Gradient descent" style="text-decoration-line: none; color: rgb(11, 0, 128); background: none rgb(255, 255, 255); font-family: sans-serif; font-size: 14px;">gradient descent</a>, <a href="https://en.wikipedia.org/wiki/Branch_and_Bound" class="mw-redirect" title="Branch and Bound" style="text-decoration-line: none; color: rgb(11, 0, 128); background: none rgb(255, 255, 255); font-family: sans-serif; font-size: 14px;">Branch and Bound</a>.<div> </div><div>https://en.wikipedia.org/wiki/Simulated_annealing </div> Info Annealing ? WTF ? 2021-01-12 11:24:49 Simulated Annealing :: What it does 2021-01-12 10:19:56 S.A. exercise: Rocket launch revisited 2021-01-12 11:47:16 S.A. How & why it works 2021-01-12 11:38:54 Simulated Annealing :: Exercise 2021-01-12 11:41:58