A solver for cone-constrained feasibility problems. The main application for this solver is implicit integration of contact dynamics.
Problems of the following form:
find x = [y, z, s]
subject to f(y,z,s; p) = 0,
z ∘ s = κ
z, s in K = R+ x Q^1 x ... x Q^k
can be optimized for
- x = [y, z, s]: decision variables
- y: primal variables
- z: dual variables
- s: slack variables
- p: problem parameters
- κ: central-path parameter
- ∘: cone product
- K: Cartesian product of convex cones; nonnegative orthant R+ and second-order cones Q are currently implemented
The solver is differentiable, and gradients of the solution (including internal solver variables) with respect to the problem parameters are efficiently computed.
using Mehrotra- @turbo loop vectorization
- in-place addition to a vector using symbolics
- sparse allocation-free linear solve
- warm-starting strategy (add user-provided slacks initialization method s = F(z) in general)
- consistency logic for efficient dynamics query
- exploit structure of the symmetric problems
- allocation-free implementation
- experiment with different relaxation scheduling strategies
- add documentation
- register package