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Simulated Annealing

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Simulated Annealing is a probabilistic method for approximating the global optimum of a given function. It is helpful especially in the case of large search space.

• Make use of randomness, random walk on a search graph.
• Transition probabilities..
• Higher probability of accepting worse solutions in the begining (high temperature).

Why is RRT efficient?

Published:

Trajectory optimization is a local method, it only explores the neighborhood of an initial seed, besides, in the direct transcription, we discretize along the trajectory instead of every dimension of the C-space, thus it scales well in high dimensional space. But RRT is a global method, what makes it efficient in exploring a collision-free path in high dimensional space?

Simulated Annealing

Published:

Simulated Annealing is a probabilistic method for approximating the global optimum of a given function. It is helpful especially in the case of large search space.

• Make use of randomness, random walk on a search graph.
• Transition probabilities..
• Higher probability of accepting worse solutions in the begining (high temperature).

Convex optimization, QP, SQP

Published:

Definition of convex function

For all ${ 0\leq t \leq 1}$ and all ${ x_{1},x_{2}\in X,}$ ${ f\left(tx_{1}+(1-t)x_{2}\right)~\leq ~tf\left(x_{1}\right)+(1-t)f\left(x_{2}\right).}$

Quadratic form in variables ${ x_1,x_2…, x_n}$ is a polynomial function $Q$, where all the terms in $Q(x_1, x_2,…, x_n)$ have order two. Quadratic functions $\neq$ convex functions.

Convex optimization, QP, SQP

Published:

Definition of convex function

For all ${ 0\leq t \leq 1}$ and all ${ x_{1},x_{2}\in X,}$ ${ f\left(tx_{1}+(1-t)x_{2}\right)~\leq ~tf\left(x_{1}\right)+(1-t)f\left(x_{2}\right).}$

Quadratic form in variables ${ x_1,x_2…, x_n}$ is a polynomial function $Q$, where all the terms in $Q(x_1, x_2,…, x_n)$ have order two. Quadratic functions $\neq$ convex functions.