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Results for “constrained optimization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1971

A stochastic constrained optimization technique and its application to detector array processing.

A stochastic projected gradient algorithm is proposed which can be used for finding a constrained optimum point for a concave or convex objective function subject to nonlinear constraints which form a connected region even when only a noisy estimate of the objective function is available. For a constraint described by a single linear equation, convergence to the constrained optimum value is proved, and the rate of convergence of the algorithm to the constrained optimum value is determined. The algorithm is applied to the nonlinear problem of obtaining automatically an array of detectors which forms a beam in a desired direction in space in the presence of interfering noise so as to maximize the SNR subject to a constraint on the super-gain ratio.

Winkler, L. P.↗
NASA NTRS1990

Control law synthesis and stability robustness improvement using constrained optimization techniques

The present generic optimization procedure for a continuous or discrete control law (of arbitrary order), which will be applicable to a multiinput-multioutput system, is upon constraining used to satisfy conflicting design requirements on the mean-square responses and stability robustness at the plant input and output. The synthesis procedure is especially suitable for flexible airframes and large space structures modeled by a high-order state-space system of equations. Analytical expressions are obtained for the gradients of the cost function, together with design constraints on the mean-square response and minimum singular value.

Mukhopadhyay, Vivekananda↗
DOE OSTI2025

Small-Signal Stability Constrained Optimal Power Flow of Inverter-Dominated Power Systems with Flexible Operation Mode Selection

Given the intermittence and low inertia nature of inverter-based resources (IBRs), modern power systems with high penetration of IBRs challenge the conventional optimal power flow (OPF) analysis and the system may experience unexpected failures if stability constraints are not incorporated. This study proposes a small-signal stability-constrained OPF (SSSC-OPF) with flexible operation mode selection between grid-forming (GFM) and grid-following (GFL) modes for IBRs to address these challenges. The approach aims to maintain system stability with a sufficient stability margin while minimizing operation costs. The effectiveness of the proposed method is validated through extensive case studies on the IEEE 14-bus system. The results demonstrate that the proposed method is able to support system-level power flow analysis, reduce generation costs, and ensure stability under various disturbances.

grid-following↗
NASA NTRS1989

Multiplier-continuation algorthms for constrained optimization

Several path following algorithms based on the combination of three smooth penalty functions, the quadratic penalty for equality constraints and the quadratic loss and log barrier for inequality constraints, their modern counterparts, augmented Lagrangian or multiplier methods, sequential quadratic programming, and predictor-corrector continuation are described. In the first phase of this methodology, one minimizes the unconstrained or linearly constrained penalty function or augmented Lagrangian. A homotopy path generated from the functions is then followed to optimality using efficient predictor-corrector continuation methods. The continuation steps are asymptotic to those taken by sequential quadratic programming which can be used in the final steps. Numerical test results show the method to be efficient, robust, and a competitive alternative to sequential quadratic programming.

Lundberg, Bruce N.↗
NASA NTRS1973

Constrained optimization of image restoration filters

A linear shift-invariant preprocessing technique is described which requires no specific knowledge of the image parameters and which is sufficiently general to allow the effective radius of the composite imaging system to be minimized while constraining other system parameters to remain within specified limits.

Riemer, T. E.↗
DOE OSTI2026

Implementing a unified solver for nonlinearly constrained optimization

SQP and interior-point methods (also referred to as Lagrange-Newton methods) typically share key algorithmic components, such as strategies for computing descent directions and mechanisms that promote global convergence. Building on this insight, we introduce a unifying framework with eight building blocks that abstracts the workflows of Lagrange-Newton methods. We then present Uno, a modular C++ solver that implements our unifying framework and allows the automatic combination of a wide range of strategies with no programming effort from the user. Uno is meant to (1) organize mathematical optimization strategies into a coherent hierarchy; (2) offer a wide range of efficient and robust methods that can be compared for a given instance; (3) enable researchers to experiment with novel optimization strategies; and (4) reduce the cost of development and maintenance of multiple optimization solvers. Uno’s software design allows user to compose new customized solvers for emerging optimization areas such as robust optimization or optimization problems with complementarity constraints, while building on reliable nonlinear optimization techniques. We demonstrate that Uno is highly competitive against state-of-the-art solvers filterSQP, IPOPT, SNOPT, MINOS, LANCELOT, LOQO, and CONOPT on a subset of 429 small problems from the CUTE collection. Uno is available as open-source software under the MIT license at https://github.com/cvanaret/Uno and via its C, Julia, Python, Fortran, and AMPL interfaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Physics-Informed Neural Networks for PDE-Constrained Optimization and Control

The goal of optimal control is to determine a sequence of inputs for maximizing or minimizing a given performance criterion subject to the dynamics and constraints of the system under observation. This work introduces Control Physics-Informed Neural Networks (PINNs), which simultaneously learn both the system states and the optimal control signal in a single-stage framework that leverages the system’s underlying physical laws. While prior approaches often follow a two-stage process-modeling, the system first and then devising its control—the presented novel framework embeds the necessary optimality conditions directly into the network architecture and loss function. We demonstrate the effectiveness of the novel methodology by solving various open-loop optimal control problems governed by analytical, one-dimensional, and two-dimensional partial differential equations (PDEs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Iterative methods in GPU-resident linear solvers for nonlinear constrained optimization

Linear solvers are major computational bottlenecks in a wide range of decision support and optimization computations. The challenges become even more pronounced on heterogeneous hardware, where traditional sparse numerical linear algebra methods are often inefficient. For example, methods for solving ill-conditioned linear systems have relied on conditional branching, which degrades performance on hardware accelerators such as graphical processing units (GPUs). To improve the efficiency of solving ill-conditioned systems, our computational strategy separates computations that are efficient on GPUs from those that need to run on traditional central processing units (CPUs). Our strategy maximizes the reuse of expensive CPU computations. Iterative methods, which thus far have not been broadly used for ill-conditioned linear systems, play an important role in our approach. In particular, we extend ideas from Arioli et al., (2007) to implement iterative refinement using inexact LU factors and flexible generalized minimal residual (FGMRES), with the aim of efficient performance on GPUs. In conclusion, we focus on solutions that are effective within broader application contexts, and discuss how early performance tests could be improved to be more predictive of the performance in a realistic environment.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Constrained optimal design of columns against buckling.

The buckling loads of straight, simply supported columns, loaded by axial thrust are maximized, subject to the restriction that the available volume of structural material is specified. In addition, a constraint is placed upon the maximum allowable prebuckling stress. Analytic solutions are presented for a variety of different cross-sectional geometries.

Frauenthal, J. C.↗
NASA NTRS1972

Constrained optimal ascent-flyback shuttle trajectories.

An optimal Space Shuttle ascent-flyback trajectory shaping capability is presented which is based on the accelerated gradient parameter optimization technique. A typical atmospheric flight branched optimization problem is analyzed which required the determination of 31 parameters. This parameter set includes the description of the vehicle attitude control angles for three branches of Shuttle flight: first stage ascent, second stage ascent, and first stage flyback. The important in-flight inequality constraints required to maintain the integrity of the vehicle are considered. Results indicate that for a launch into a 55 deg inclined ellipse, a 13% increase in payload can be realized by using optimal control in the first-stage ascent rather than the conventional gravity-turn steering.

Kamm, J. L.↗
NASA NTRS1973

Constrained optimization of image restoration filters.

A preprocessing method to correct for image degradation is proposed which can be thought of as a generalization and extension of previous work by Smith (1966) and Stuller (1972). This method accomodates the problem of noncircularly symmetric imaging system point-spread functions, provides for controlled extent of the preprocessing filter to minimize distortion due to transients resulting from truncation errors and edge effects, can be used with various kinds of system noise, and can be readily extended to provide constraint of other system parameters. The analysis relates to a line-scanner system, although it is applicable in principle to many other system configurations.

Riemer, T. E.↗
NASA NTRS1975

Constrained optimal controller for linear systems with state and control dependent disturbance

The problem is posed with the additional constraints that the dynamic controller uses only noise-corrupted outputs, and that its dimension is significantly lower than that of a Kalman filter. The unknown disturbance is viewed as an adversary which tries to maximize a performance criterion: a criterion that the controller gains attempt to minimize. The optimal controller gains are determined by solving a nonlinear matrix two-point boundary value problem.

Basuthakur, S.↗