The Mathworks, Inc.

Depuy Spine, Inc.

Rakesh Kumar

Mary Ann Branch Freeman

Amy Kidd

Marcelo Marazzi

Marjorie Berman

Tara Scott

Thomas Lane

Jared Carlson

Thomas V Doherty

Raymond F Murphy

An adaptable graphical user interface is provided. The interface may include a setup region to display a group of available solvers to a user and to display parameters for at least one of the group of solvers to the user. The interface may include an options region to display a first group of active options to a user without requiring a user input for the first group of active options, where the first group of active options is determined based on a selected one of the group of solvers. The options region may further display a second group of active options to the user when another one of the solvers is selected by the user, where the second group of active options differs from the first group of active options.

User configured optimizer

An Augmented Lagrangian Pattern Search (ALPS) algorithm that attempts to solve a non-linear optimization problem subject to non-linear, linear, and, bound constraints is discussed. The present invention utilizes information from the linear and bound constraints, formulates sub-problems using Lagrange parameter estimates and appropriate penalty parameters (using a log barrier), and provides a robust update formulae for parameters which guides the algorithm towards a minimum. The present invention solves a general non-linear optimization problem without using any slack variables to convert the inequality constraints to equality constraints or equality constraints to inequality constraints.

System and method for performing non-linear constrained optimization with an augmented lagrangian pattern search

An instrument for inserting, adjusting and engaging an implant, such as a polyaxial screw of a spinal fixation system, includes at least one retractable tab for engaging a corresponding recess on the implant and a shaft that moves relative to the retractable tab. The movable shaft selectively moves the tab between an expanded position for engaging the recess and a retracted position out of engagement with the recess. The shaft selectively engages a portion of the implant to rigidify the implant after the retractable tab engages the recess. The axially extending shaft may be disposed within an axially extending passageway of a body assembly. A rotatable collar surrounding the body assembly is coupled to the shaft for moving the shaft relative to the retractable tab.

Instrument for inserting, adjusting and removing a surgical implant

An augmented Lagrangian genetic algorithm that may be used to generate solutions for optimization problems subject to linear, bound, and non-linear constraints is discussed. The augmented Lagrangian genetic algorithm uses an adaptive mutation operator to separately handle the linear, and bound constraints, and uses an augmented Lagrangian framework to handle non-linear constraints. The non-linear constraints are handled by creating a sub-problem without the linear and bound constraints and solving the sub-problem using Lagrange parameter estimates and a penalty factor. The exclusion of the linear constraints and boundary constraints from the sub-problem allows the sub-problem to be resolved in a more effective manner than is possible using conventional techniques.

System and method for performing non-linear constrained optimization with a genetic algorithm

In one embodiment, a method of providing a graphical user interface (GUI) for a plurality of optimization solvers is provided. The method includes providing the GUI for the plurality of optimization solvers, the GUI including a control to select a solver from the plurality of solvers; receiving a selection of a first solver from the plurality of optimization solvers; and displaying in the GUI contents tailored to the first solver, the GUI contents including an objective function, wherein the objective function is in an expression in an array-based language.

Method and product of manufacture for the recommendation of optimizers in a graphical user interface for mathematical solvers

An adaptive mutation operator for a genetic algorithm that programmatically mutates individuals in a constrained optimization for a modeled system is discussed. The mutation operator takes into account linear and bound constraints in generating new mutated individuals. The mutation operator generates random mutation direction vectors and random initial step sizes. A mutated individual is generated and moved along a randomly chosen mutation direction vector a distance equal to the initial step size. The generated mutated individual is compared to the linear and bound constraints. In the event the generated mutated individual is located in an infeasible region, the illustrative embodiment of the present invention automatically adjusts the step size to a smaller value and generates another mutated individual along the chosen mutation direction vector. The process iterates until the generated individual is within the feasible region. The number of available valid mutation directions increases as the step size decreases.

System and method for the use of an adaptive mutation operator in genetic algorithms

In performing a pattern search algorithm to find an optimal solution for an optimization, using a set of positive basis vectors that is non-maximal like a set of minimal positive basis vectors as search directions can sometimes causes the algorithm to converge at a non-optimal solution, like a horse saddle. The present invention provides a method to overcome this deficiency by rotating the vectors and hence increase the number of search directions.

Growing community of inventors

Framingham, MA, United States of America

Rakesh Kumar