Nonlinear programming: theory and algorithms / Mokhtar S. Bazaraa, Hanif D. Sherali, Polynomial-Time Interior Point Algorithms for Linear. Linear Programming and Network Flows, Bazaraa. Luenberger David G. - - Linear and Nonlinear Programming (2nd Edition) Numerical Methods for Unconstrained Optimization and Nonlinear Equations Classics in Applied Mathematics. Bazaraa, M. S. Nonlinear programming: theory and algorithms / Mokhtar S. Bazaraa, Hanif D. Sherali, C. M. Shettyrd ed. p. cm. “Wiley-Interscience.” Includes.
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Nonlinear Model Predictive Control: Theory and Algorithms · Read more · Optimization Theory and Methods - Nonlinear Programming. Read more. COMPREHENSIVE COVERAGE OF NONLINEAR PROGRAMMING THEORY AND ALGORITHMS, THOROUGHLY REVISED AND. Nonlinear programming: Theory and algorithms. Book Reviews and illustrations. A great variety of exercises at the end of every chapter enables the reader not.
The ARL focus on the definition of biological injury in terms relevant to materials and engineering is a necessary step in moving this critical area forward and may be unique in the field.
The focus at ARL on identifying the critical size scale of injury is correct, and the group emphasis on the translation of animal data to humans is necessary and positive.
Internal to ARL, principal investigators PIs demonstrated greater awareness of work done in the areas of threat warning and countermeasures. The quality of the programs will continue to benefit from even deeper and more frequent collaborations both internal and external to ARL to foster rapid innovation with operational and contextual relevance. Penetration, Armor, and Adaptive Protection ARL continues to demonstrate a strong record of achievement in the fundamental and applied sciences and the engineering of penetration, armor, and adaptive protection.
The ongoing work continues to highlight how ARL is building on its history of excellence to provide the knowledge basis for future Army needs in the area of warfighter protection. This is critical and core competency that underlies Army capabilities. A start has been made in the uncertainty quantification UQ area of research, but there is a long way to go.
It was unclear what the level of effort of research is or the number of people involved. ARL needs to continue an emphasis in UQ. Further, ARL needs to pursue the integration of UQ into its data-to- decision workflow that includes modeling and simulation, experimentation, and design. Disruptive Energetics and Propulsion Technologies The focus of research in the disruptive energetics and propulsion technologies program is the exploration and development of new and novel energetic materials that can potentially revolutionize munitions and propulsion systems by enhancing energy release and lethality greater than traditional energetic materials.
The review of the disruptive energetic material and propulsion technology covered the areas of new material synthesis, small-scale energetic material characterization using laser flyers and rapid heating diagnostics, experimental studies of structural bond energy release nanomaterials, quantum to force field molecular modeling, multiscale coarse-grain modeling of energetic composites, and multiphase rocket and gun propellant modeling.
The ongoing work in this review focused on how ARL continues to lead the Army in its core competencies of blast survivability, ballistic penetration, and protection technologies. These programs, taken together, address this goal in myriad efforts to better understand the physics of failure and utilize this understanding to quantify the effects of threats, develop more lethal threats, and design more resilient strategies to defeat threats.
Flight Guidance, Navigation, and Control Overall, the projects in flight guidance, navigation, and control demonstrated very competent, and in some cases excellent, research.
The fundamental problems addressed include maneuverability and terminal guidance to imperfectly located, moving, or protected targets to achieve the operational impact of a deep magazine with precision capability. Many of the projects would benefit from end-to-end system performance modeling to frame performance requirements and technical and parametric goals. This observation also applies to the low- cost canard activator discussion. With a systems view of the impact to the overall cost balance, ARL could identify where improvements provide the most leverage in achieving desired performance at an affordable cost.
The ARL flight guidance, navigation, and control research program leads among similar institutions in its focus on Army-relevant problems with the potential for breakthrough operational capability and acceleration.
The research team demonstrates exceptional competence in executing a research program focused on incremental advances that could revolutionize Army precision and lethality capability while breaking the cost curve. Included in this approach are technological advances that support information acquisition, reasoning with such information, and support for decision-making activities such as collaborative communications.
Sensing and Effecting Sensing and effecting research projects covered thematic areas of nonimaging sensors acoustic, electric, magnetic, seismic , radar sensing and signal processing, image and video analytics, sensor and data fusion, and machine learning. Noteworthy programs include electric and magnetic field sensing, research on the next-generation improvised explosive device and landmine detection platform, computational advances in electric field modeling, cross-modal face recognition, and innovative approaches to fuse textual context with image features to improve machine learning.
The work was generally of high scientific quality, with a balance between theoretical and experimental work, as well as evidence of transition into practice. System Intelligence and Intelligent Systems System intelligence and intelligent systems SIIS research spans areas of information understanding, information fusion, and computational intelligence. This research has led to an improved understanding of complex environments and streaming data related to navigation, exploration, and mapping of the physical world.
The work on unsupervised learning of semantic labels in streaming data, and the synergies between visual analysis and efficient exploration of environments is noteworthy. Ongoing collaboration among researchers within ARL as well as on the outside on information analysis in SIIS to decision support in human-information interaction , is likely to yield good dividends.
Human-Information Interaction Human-information interaction HII is a new program in the Information Sciences Campaign and has been in operation since , bringing together researchers from disparate disciplines and technical backgrounds.
The objective of HII research at ARL is to develop models, methods, and understanding of data and information generated by humans and intelligent agents in a complex, multigenre network environment. Atmospheric Sciences The atmospheric sciences research portfolio of the Battlefield Environments Division seeks to improve environmental understanding of the planetary boundary layer PBL and processes that operate on small spatial and temporal scales, and on developing appropriate environmental intelligence tools for deployed soldiers to use in austere, complex operating environments.
Promising research projects reviewed included detection and characterization of chemical aerosols, acoustic and infrasound sensing, development and fielding of a meteorological sensor array at White Sands Missile Range WSMR , and advances in small-scale atmospheric model development, verification, and validation.
Networks and Communications The networks and communications research portfolio focuses on understanding and exploiting interactions of information with sociotechnical networks, particularly communications, and command and control networks. The research comprises three broad topical areas: channels and protocols, control and behavior, and information delivery. Human-machine teaming is a growing topic in all three topical areas. Since the previous review in , significant progress has been made in many of these areas of research.
Cybersecurity: Detection and Agility Cyberattackers, both human and intelligent agents, pose a significant threat to Army information systems and networks. Understanding how adversarial elements interact with information is important, as is the analysis and understanding of adversary resources, learning and recognizing adversary tactics, and ultimately anticipating adversarial activity to mitigate the effects of cyberattacks.
Risk characterization is another important element of cybersecurity research. The overall quality of research was good, with some projects characterized as excellent. Substantial progress has been made in each of the three areas since the last review in Looking forward, the campaign would benefit from three activities designed to focus effort and ensure success.
By creating and sharing white papers on the state of the art, the campaign could identify national thought leaders whose research, insights, and ideas would inform and guide research and development. Second, the campaign could convert conceptual diagrams of future battlefields to tangible work plans, emphasizing those focused activities likely to maximize return on investment.
Third, all projects would benefit from clearer metrics for project success and associated project exit strategies, including transitions that maximize Army benefits. To serve the long-term Army needs for quantum secure communication and networking, ARL has also been exploring the potential range of uses of quantum computing and networking through modeling and simulation.
Data-Intensive Sciences This campaign has focused on applied machine learning ML , neuromorphic computing, and cooperative multiagent control using deep reinforcement learning. In addition to software testing and evaluation for the IBM TrueNorth processor, in a collaborative partnership with Stanford University, the campaign extended the multiagent-setting methods previously used to train a single agent using cooperative reinforcement learning, achieving state-of-the-art performance in several applications.
In addition, the campaign has addressed several Army-relevant ML needs, including planetary gearbox analysis as a proactive approach to preventative maintenance by automated generation of features. Three data-intensive computing projects high-throughput electrolyte modeling, discovering and quantifying atomistic defects in large data sets for assessing nanocrystalline aluminum, and computational technologies for the reduction of highly nonlinear and multiscale solid mechanics and structural dynamics models are exploiting the same ARL-developed software architecture for distributed simulation.
Two notable examples of such work were a computational framework for scale bridging with application to multiscale modeling of RDX explosives, and models for integrated computational materials engineering for polycrystalline materials. Interactions with ARL scientists and engineers, including research presentations, posters, and laboratory visits, were useful in terms of assessing the quality of ARL research.
Several outstanding research projects are noteworthy. The project on stimuli-responsive interface mechanics for nanocomposites focused on improving damping by modifying the polymer-fiber interface and using stimuli-responsive photoreactive molecules for functionalizing the carbon nanotubes.
Theoretical concepts for controlling system dynamics by using a linear state space model were elegantly explained in the project on Gramian-based control of unmanned aircraft system UAS disturbances. The project on energy-efficient multimodal flight is focused on designing and testing a tiltrotor vehicle weighing less than g. The project principally addresses the dynamics and control of the system and has successfully achieved the transition from hover to forward flight.
The research on low-rank representation learning of action attributes flexibility and extensibility in focusing on human action attributes is outstanding.
The research on autonomous mobile information collection using a value of information-enriched belief approach is also outstanding. The ARL has world-class research equipment, experimental facilities, and computational resources, including a spray combustion research laboratory, small engine altitude research facility, and high- temperature propulsion materials laboratory.
Mentoring efforts appear to be effective. There are several opportunities for greater advancement in the campaign research productivity. Researchers working on the project on stimuli-responsive interface mechanics for nanocomposites could estimate the amount of damping created by modifications of the interface between the nano- reinforcements and the surrounding matrix.
The investigators could consider the following: examining a tiltrotor version of the recently ended UHA helicopter air loads program seeking improved tiltrotor modeling tools to accurately predict whirl-flutter stability; exploring other CFD solvers toward validating FUN3D results; and identifying desirable tiltrotor features that require better computational models e. Increased use of Army soldier field experiences and scenarios, robots, and more relevant data sets could enhance all research in this campaign.
Increased ARL journal publications and presence at and participation in conferences would help to define the problem set. Overall, the technical quality of the work is high. In particular, the group has worked to identify technical and theoretical gaps and to align resources to solve specific needs.
The technical quality of enabling technology and instrumentation was especially high. In general, the group uses strong experimental techniques and appropriate modeling approaches. Still, as new research areas are broached, the work would benefit from consultation with appropriate experts.
Human Variability The analytical abilities and techniques of the Human Variability program in general are strong. The electroencephalogram EEG -related technical expertise is excellent. The source localization methods being developed are interesting and provide a good approach to go beyond simple subtractive methods of analysis. The Human Variability projects have made progress since the last review by continuing to publish findings in the scientific literature and present findings at conferences.
Humans in Multiagent Systems Overall, the technical quality of the work is good, and methodologies that are used to explore the research questions are appropriate. Throughout the description of the research in this area, there were many examples of good interdisciplinary collaboration to support broad-ranging questions among computer scientists, cognitive scientists, and human factors psychologists and engineers, such as in the work on trust in robotic transportation systems.
The Humans in Multiagent Systems team leverages the foundational research of colleagues in the Human Variability and Real-World Behavior programs to inform their applied work as well as collaboration with operational warfighters, at Fort Bragg, for example. Financial Support Students joining the Ph.
Qualifications mention at i above with two years of Research Experience: Rs. Highlights Full time PhD students are encouraged to present their research work at international conferences.
Financial support of up to Rs. Additional funding may also be provided. Over and above the monthly assistantship, an additional fellowship of Rs. A candidate can receive only one of the above mentioned two additional fellowships. You can see the background of our existing Ph.
Nanoelectronics, Semiconductor Devices, Nanotechnology 2. Biomedical Engineering, Human Computer Interaction 4. Control Systems, Estimation Theory 7. Power Systems and Smart Grid 8. Deep Learning, High Performance Computing Cyber Physical Systems Heat Transfer and Thermofluid sciences 5.
Combustion, Energetic nanomaterials 7. Robotics - Controls; Drones 9. Manufacturing - Laser based manufacturing. Optoelectronic materials, Solar cell, Transparent conductor 2. Van der Waal's material based membranes for desalination. Graphene laminates based nanofluidic channels for gas separation. Small-scale active matter, Intracellular particle dynamics, Smart nano bio sensors.