feat: Point coordinate regulators

glen commented

2025-09-20 08:56:24 +00:00

Owner

Implements regulators for the Euclidean coordinates of Point entities,
automatically creating all three of them for each added point entity. When
such a regulator is set, it freezes the corresponding representation
coordinate to the set point. In addition, if all three coordinates of a
given Point are set, the coradius coordinate (which holds the norm of the
point) is frozen as well.

Note that a PointCoordinateRegulator must be created with a Point as the
subject. This commit modifies HalfCurvatureRegulator analogously, so that
it can only be created with a Sphere.

A couple of prospective issues that should be filed in association with
this commit:

The new coordinate regulators create redundant display information with
the raw representation coordinates of a point that are already shown in
the outline view.
The optimization status of these regulators together with HalfCurvature
regulators (i.e., the ones implemented by freezing coordinates) is different
from InversiveDistance regulators when an Assembly is unrealizable: the
frozen-coordinate constraints will be "hard" in that they will be forced
to precisely equal their set point, whereas the distance regulators are
"soft" in that they can be relaxed from their set points in an effort to
minimize the loss function of the configuration as compared to the values
of the constraints. Perhaps at some point we should/will have a mechanism
to specify the softness/hardness of constraints, but in the meantime,
there should not be two different categories of constraints. Suppose we
decide that by default that all constraints are soft. Then the optimizer
should be able to search changing, for example, the radius of a
curvature-constrained sphere, so as to minimize the loss function (for a
loss that would therefore presumably have a term akin to the square of the
difference between the specified and actual half-curvature of the sphere).
For example, suppose you specify that the half-curvature of a sphere is 1
(so it has radius 1/2) but that its distance to a point is -1. These
constraints cannot be satisfied, so the optimization fails, presumably
with the point at the sphere center, and the sphere with radius 1/2.
So all of the loss is concentrated in the difference between the actual
point-sphere distance being -1/2, not -1. It would be more appropriate
(in the all-soft constraint regime) to end up at something like a sphere of
half-curvature 1/√2 with the point at the center, so that the loss is split
between both the half-curvature and the distance to the sphere being off by
1 - 1/√2. (At a guess, that would minimize the sum of the squares of the
two differences.)

Implements regulators for the Euclidean coordinates of Point entities, automatically creating all three of them for each added point entity. When such a regulator is set, it freezes the corresponding representation coordinate to the set point. In addition, if all three coordinates of a given Point are set, the coradius coordinate (which holds the norm of the point) is frozen as well. Note that a PointCoordinateRegulator must be created with a Point as the subject. This commit modifies HalfCurvatureRegulator analogously, so that it can only be created with a Sphere. A couple of prospective issues that should be filed in association with this commit: * The new coordinate regulators create redundant display information with the raw representation coordinates of a point that are already shown in the outline view. * The optimization status of these regulators together with HalfCurvature regulators (i.e., the ones implemented by freezing coordinates) is different from InversiveDistance regulators when an Assembly is unrealizable: the frozen-coordinate constraints will be "hard" in that they will be forced to precisely equal their set point, whereas the distance regulators are "soft" in that they can be relaxed from their set points in an effort to minimize the loss function of the configuration as compared to the values of the constraints. Perhaps at some point we should/will have a mechanism to specify the softness/hardness of constraints, but in the meantime, there should not be two different categories of constraints. Suppose we decide that by default that all constraints are soft. Then the optimizer should be able to search changing, for example, the radius of a curvature-constrained sphere, so as to minimize the loss function (for a loss that would therefore presumably have a term akin to the square of the difference between the specified and actual half-curvature of the sphere). For example, suppose you specify that the half-curvature of a sphere is 1 (so it has radius 1/2) but that its distance to a point is -1. These constraints cannot be satisfied, so the optimization fails, presumably with the point at the sphere center, and the sphere with radius 1/2. So all of the loss is concentrated in the difference between the actual point-sphere distance being -1/2, not -1. It would be more appropriate (in the all-soft constraint regime) to end up at something like a sphere of half-curvature 1/√2 with the point at the center, so that the loss is split between both the half-curvature and the distance to the sphere being off by 1 - 1/√2. (At a guess, that would minimize the sum of the squares of the two differences.)

glen added 1 commit 2025-09-20 08:56:24 +00:00

feat: Point coordinate regulators

/ test (pull_request) Successful in 3m35s

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ecbbe2068c

Implements regulators for the Euclidean coordinates of Point entities,
  automatically creating all three of them for each added point entity. When
  such a regulator is set, it freezes the corresponding representation
  coordinate to the set point. In addition, if all three coordinates of a
  given Point are set, the coradius coordinate (which holds the norm of the
  point) is frozen as well.

  Note that a PointCoordinateRegulator must be created with a Point as the
  subject. This commit modifies HalfCurvatureRegulator analogously, so that
  it can only be created with a Sphere.

  A couple of prospective issues that should be filed in association with
  this commit:
  * The new coordinate regulators create redundant display information with
    the raw representation coordinates of a point that are already shown in
    the outline view.
  * The optimization status of these regulators together with HalfCurvature
    regulators (i.e., the ones implemented by freezing coordinates) is different
    from InversiveDistance regulators when an Assembly is unrealizable: the
    frozen-coordinate constraints will be "hard" in that they will be forced
    to precisely equal their set point, whereas the distance regulators are
    "soft" in that they can be relaxed from their set points in an effort to
    minimize the loss function of the configuration as compared to the values
    of the constraints. Perhaps at some point we should/will have a mechanism
    to specify the softness/hardness of constraints, but in the meantime,
    there should not be two different categories of constraints. Suppose we
    decide that by default that all constraints are soft. Then the optimizer
    should be able to search changing, for example, the radius of a
    curvature-constrained sphere, so as to minimize the loss function (for a
    loss that would therefore presumably have a term akin to the square of the
    difference between the specified and actual half-curvature of the sphere).
    For example, suppose you specify that the half-curvature of a sphere is 1
    (so it has radius 1/2) but that its distance to a point is -1. These
    constraints cannot be satisfied, so the optimization fails, presumably
    with the point at the sphere center, and the sphere with radius 1/2.
    So all of the loss is concentrated in the difference between the actual
    point-sphere distance being -1/2, not -1. It would be more appropriate
    (in the all-soft constraint regime) to end up at something like a sphere of
    half-curvature 1/√2 with the point at the center, so that the loss is split
    between both the half-curvature and the distance to the sphere being off by
    1 - 1/√2. (At a guess, that would minimize the sum of the squares of the
    two differences.)

glen commented

2025-09-20 08:57:49 +00:00

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Owner

@Vectornaut once we are past finalizing the configuration of the Impossolid for the build, please review this PR, thanks.

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