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Merge pull request #612 from qleonardolp:patch-1

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2022-12-03 10:49:22 -08:00
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@@ -71,7 +71,7 @@ and the resulting equations of motion are difficult to characterize. To be fair,
worst-case performance and does not mean that solving the LCP quickly is impossible in practice. Still, convex
optimization has well-established advantages. In MuJoCo we have observed that for typical robotic models, 10 sweeps of a
projected Gauss-Seidel method (PGS) yield solutions which for practical purposes are indistinguishable from the global
minimum. Of course there are problems that are much harder to solve numerically, even though the are convex, and for
minimum. Of course there are problems that are much harder to solve numerically, even though they are convex, and for
such problems we have conjugate gradient solvers with higher-order convergence.
The requirements for computational efficiency are different depending on the use case. If all we need is real-time