2,302 research outputs found
A computer algebra user interface manifesto
Many computer algebra systems have more than 1000 built-in functions, making
expertise difficult. Using mock dialog boxes, this article describes a proposed
interactive general-purpose wizard for organizing optional transformations and
allowing easy fine grain control over the form of the result even by amateurs.
This wizard integrates ideas including:
* flexible subexpression selection;
* complete control over the ordering of variables and commutative operands,
with well-chosen defaults;
* interleaving the choice of successively less main variables with applicable
function choices to provide detailed control without incurring a combinatorial
number of applicable alternatives at any one level;
* quick applicability tests to reduce the listing of inapplicable
transformations;
* using an organizing principle to order the alternatives in a helpful
manner;
* labeling quickly-computed alternatives in dialog boxes with a preview of
their results,
* using ellipsis elisions if necessary or helpful;
* allowing the user to retreat from a sequence of choices to explore other
branches of the tree of alternatives or to return quickly to branches already
visited;
* allowing the user to accumulate more than one of the alternative forms;
* integrating direct manipulation into the wizard; and
* supporting not only the usual input-result pair mode, but also the useful
alternative derivational and in situ replacement modes in a unified window.Comment: 38 pages, 12 figures, to be published in Communications in Computer
Algebr
Satisfiability of General Intruder Constraints with and without a Set Constructor
Many decision problems on security protocols can be reduced to solving
so-called intruder constraints in Dolev Yao model. Most constraint solving
procedures for protocol security rely on two properties of constraint systems
called monotonicity and variable origination. In this work we relax these
restrictions by giving a decision procedure for solving general intruder
constraints (that do not have these properties) that stays in NP. Our result
extends a first work by L. Mazar\'e in several directions: we allow non-atomic
keys, and an associative, commutative and idempotent symbol (for modeling
sets). We also discuss several new applications of the results.Comment: Submitted to the Special issue of Information and Computation on
Security and Rewriting Techniques (SecReT), 2011. 59 page
A low complexity scaling method for the Lanczos Kernel in fixed-point arithmetic
We consider the problem of enabling fixed-point implementation of linear algebra kernels on low-cost embedded systems, as well as motivating more efficient computational architectures for scientific applications. Fixed-point arithmetic presents additional design challenges compared to floating-point arithmetic, such as having to bound peak values of variables and control their dynamic ranges. Algorithms for solving linear equations or finding eigenvalues are typically nonlinear and iterative, making solving these design challenges a nontrivial task. For these types of algorithms, the bounding problem cannot be automated by current tools. We focus on the Lanczos iteration, the heart of well-known methods such as conjugate gradient and minimum residual. We show how one can modify the algorithm with a low-complexity scaling procedure to allow us to apply standard linear algebra to derive tight analytical bounds on all variables of the process, regardless of the properties of the original matrix. It is shown that the numerical behavior of fixed-point implementations of the modified problem can be chosen to be at least as good as a floating-point implementation, if necessary. The approach is evaluated on field-programmable gate array (FPGA) platforms, highlighting orders of magnitude potential performance and efficiency improvements by moving form floating-point to fixed-point computation
Combining Enumeration and Deductive Techniques in order to Increase the Class of Constructible Infinite Models
AbstractA new method for building infinite models for first-order formulae is presented. The method combines enumeration techniques with existing deductive (in a broad sense) ones. Its soundness and completeness w.r.t. the class of models that can be represented by equational constraints are proven. This shows that the use of enumeration techniques strictly increases the power of existing methods for building Herbrand models that are not complete in this sense. Some strategies are proposed to reduce the search space. We give examples and show how to use this approach for building interactively a model of a formula introduced by Goldfarb in his proof of the undecidability of the Gödel class with identity. This formula is satisfiable but has no finite model
Lasso for hierarchical polynomial models
In a polynomial regression model, the divisibility conditions implicit in
polynomial hierarchy give way to a natural construction of constraints for the
model parameters. We use this principle to derive versions of strong and weak
hierarchy and to extend existing work in the literature, which at the moment is
only concerned with models of degree two. We discuss how to estimate parameters
in lasso using standard quadratic programming techniques and apply our proposal
to both simulated data and examples from the literature. The proposed
methodology compares favorably with existing techniques in terms of low
validation error and model size
Custom optimization algorithms for efficient hardware implementation
The focus is on real-time optimal decision making with application in advanced control
systems. These computationally intensive schemes, which involve the repeated solution of
(convex) optimization problems within a sampling interval, require more efficient computational
methods than currently available for extending their application to highly dynamical
systems and setups with resource-constrained embedded computing platforms.
A range of techniques are proposed to exploit synergies between digital hardware, numerical
analysis and algorithm design. These techniques build on top of parameterisable
hardware code generation tools that generate VHDL code describing custom computing
architectures for interior-point methods and a range of first-order constrained optimization
methods. Since memory limitations are often important in embedded implementations we
develop a custom storage scheme for KKT matrices arising in interior-point methods for
control, which reduces memory requirements significantly and prevents I/O bandwidth
limitations from affecting the performance in our implementations. To take advantage of
the trend towards parallel computing architectures and to exploit the special characteristics
of our custom architectures we propose several high-level parallel optimal control
schemes that can reduce computation time. A novel optimization formulation was devised
for reducing the computational effort in solving certain problems independent of the computing
platform used. In order to be able to solve optimization problems in fixed-point
arithmetic, which is significantly more resource-efficient than floating-point, tailored linear
algebra algorithms were developed for solving the linear systems that form the computational
bottleneck in many optimization methods. These methods come with guarantees
for reliable operation. We also provide finite-precision error analysis for fixed-point implementations
of first-order methods that can be used to minimize the use of resources while
meeting accuracy specifications. The suggested techniques are demonstrated on several
practical examples, including a hardware-in-the-loop setup for optimization-based control
of a large airliner.Open Acces
Computer Architectures to Close the Loop in Real-time Optimization
© 2015 IEEE.Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as fast optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other
UQTools: The Uncertainty Quantification Toolbox - Introduction and Tutorial
UQTools is the short name for the Uncertainty Quantification Toolbox, a software package designed to efficiently quantify the impact of parametric uncertainty on engineering systems. UQTools is a MATLAB-based software package and was designed to be discipline independent, employing very generic representations of the system models and uncertainty. Specifically, UQTools accepts linear and nonlinear system models and permits arbitrary functional dependencies between the system s measures of interest and the probabilistic or non-probabilistic parametric uncertainty. One of the most significant features incorporated into UQTools is the theoretical development centered on homothetic deformations and their application to set bounding and approximating failure probabilities. Beyond the set bounding technique, UQTools provides a wide range of probabilistic and uncertainty-based tools to solve key problems in science and engineering
- …