Michael Gleicher and Andrew Witkin. Supporting numerical computations in interactive contexts. Technical report, Carnegie Mellon University, School of Computer Science, Pittsburgh, PA 15213-3891, 1993.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....of the post WIMP interface is to find and characterize Chapter 4: Design Issues in Spatial Input 91 appropriate mappings from high degree of freedom input devices to high degree offreedom input tasks. Applications such as 3 Draw [141] and abstractions such as Gleicher s snaptogether math [64] make good initial progress toward providing constrained input in 3D, but the general spatial input constraint problem, and the issue of providing appropriate feedback in particular, is still a challenging area for future research. 4.12 Control metaphors Ware [177] identifies three basic ....

Gleicher, M., "Supporting Numerical Computations in Interactive Contexts," Graphics Interface `93.

.... in O(dimp) iterations (resulting in a quadratic time solution) Since this is an interactive system, with the user working directly with the surface, there is no need to show the user the final solution if they are still interacting it is considered more important to show the user feedback [GW93] As a result, the solver is only run a few steps before being redisplayed. During this time, the constraints may drift slightly, and the surface may become somewhat unfair. Once the user releases the surface, the solver can catch up, and display the iterations toward the final surface over the ....

Michael Gleicher and Andrew Witkin. Supporting numerical computations in interactive contexts. In Proceedings of Graphics Interface '93, pages 138--146, Toronto, Ontario, Canada, May 1993. Canadian Information Processing Society. 118

....finds a local minimum, ignorant of the global search space. Other systems solvers, including Juno [112] and Juno 2 [76] use (multidimensional) NewtonRhapson iteration to exploit derivative information. Some systems use automatic differentiation to relieve the user from specifying derivatives [58], while others simply limit the set of functions known to the underlying solver. Juno 2 s solver performs numerous optimizations, including propagation of known state, unification of pair constraints, unpacking pair constraints to primitive constraints (thus separating numeric constraints from ....

....value decomposition can be useful for under constrained systems in place of Gaussian elimination) can the techniques perform computations robustly. Bramble and its Snap Together Mathematics package provides some of these tools in the context of Whisper an extensible Scheme like language [54, 58]. 9 One of the more promising uses of iterative techniques is exemplified by the GLIDE interactive graph layout system [122] GLIDE gives up on the difficult problem of global optimization of a graph layout. Instead, it focuses on exploiting the solver s strength local minimization and ....

Michael Gleicher and Andrew Witkin. Supporting numerical computations in interactive contexts. In Graphics Interface 1993, 1993.

....Interactive satisfaction algorithms Greg J. Badros Other systems solvers, including Juno and Juno 2 [Nel85, HN94] use (multidimensional) Newton Rhapson iteration to exploit derivative information. Some systems use automatic differentiation to relieve the user from specifying derivatives [GW93] and others simply limit the set of functions known to the underlying solver. Juno 2 s solver performs numerous optimizations, including propagation of known state, unification of pair constraints, unpacking (to primitive constraints separating numeric constraints from non numeric constraints) ....

....decomposition can be useful for under constrained systems in place of Gaussian elimination) can the techniques perform computations robustly. Bramble and its Snap Together Mathematics package provides some of these tools in the context of Whisper an extensible Scheme like language [Gle93, GW93] 15 One of the more promising uses of iterative techniques is exemplified by the glide interactive graph layout system [RMS97] glide gives up on the di#cult (and ine#cient) problem of global optimization of a graph layout. Instead, it focuses on exploiting the solver s strength local ....

Michael Gleicher and Andrew Witkin. Supporting numerical computations in interactive contexts. In Graphics Interface 1993, 1993.

.... equations, can be used [33, 45, 46, 67] The Oak system realizes real time constraint satisfaction by employing linear approximation for quadratic constraints [84] Snap together mathematics is a technique for approximation of constraint systems based on dynamic models and solves them efficiently [26, 27, 28]. Local propagation has often been applied to ordinary constraint systems. Sketchpad is the first GUI system that adopts a local propagation technique called one pass method (it also uses numerical methods on failure of the one pass method) 79] Most systems employ one way constraints, whose ....

Gleicher, M. and A. Witkin, "Supporting Numerical Computations in Interactive Contexts," in Graphics Interface '93, May 1993, pp. 138-- 145.

....also their derivatives. Symbolic approaches that generate code and require recompilation are undesirable. We would rather generate these systems of equations on the fly in response to user interactions. Thus, we use an approach first introduced by [WK88] and further developed and encapsulated in[GW93]. This snap togethermath system provides data structures that represent functional elements that are wired together with composition. We extend this idea to wire together entire Constraint Based Motion Adaptation 16 motions, allowing us to build constraint problems using primitives such as ....

Michael Gleicher and Andrew Witkin. Supporting Numerical Computations in Interactive Contexts. Proceedings of Graphics Interface 93.

....respond to a simple protocol. This simplifies applications by reducing the need for special math objects which must be allocated and maintained. Snap Together Mathematics uses a specially designed sparse matrix representation and does extensive caching. Snap Together Math is described in detail in [12], and a previous version is described in [9] 7 Putting it Together A wide variety of graphical applications might employ constraints. Any of these applications will face the same issues previously described. Fortunately, the solutions proposed are general enough to apply across many ....

Michael Gleicher and Andrew Witkin. Supporting numerical computations in interactive contexts. In Tom Calvert, editor, Proceedings Graphics Interface, May 1993. To Appear.

....as a directed acyclic graph 2 with composed functions at the nodes, and arcs representing composition. Our approach to providing function composition in the dynamic setting of interactive applications is to provide a tools for managing these function graph structures. In Snap Together Math[12], function elements are wired together to make more complicated functions. Evaluating the values and derivatives of an expression involves traversing the graph. To compute a value, a node requests the values from its predecessors and then performs its local function on these results. The chain ....

Michael Gleicher and Andrew Witkin. Supporting numerical computations in interactive contexts. In Tom Calvert, editor, Graphics Interface, pages 138--145, May 1993.

No context found.

Michael Gleicher and Andrew Witkin. Supporting numerical computations in interactive contexts. Technical report, Carnegie Mellon University, School of Computer Science, Pittsburgh, PA 15213-3891, 1993.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC