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ORCIDMathukumalli Vidyasagar
M. Vidyasagar
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- affiliation: University of Texas at Dallas, USA
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2020 – today
- 2024
[j49]Rajeeva Laxman Karandikar, Mathukumalli Vidyasagar
:
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications. J. Optim. Theory Appl. 203(3): 2412-2450 (2024)- 2023
- [j48]M. Vidyasagar:
Convergence of stochastic approximation via martingale and converse Lyapunov methods. Math. Control. Signals Syst. 35(2): 351-374 (2023) - [i13]Mathukumalli Vidyasagar:
A Tutorial Introduction to Reinforcement Learning. CoRR abs/2304.00803 (2023) - [i12]Rajeeva Laxman Karandikar, M. Vidyasagar:
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applications. CoRR abs/2312.02828 (2023) - 2022
- [j47]Shantanu Prasad Burnwal, Mathukumalli Vidyasagar:
Modified Error Bounds for Matrix Completion and Application to RL. IEEE Control. Syst. Lett. 6: 1916-1921 (2022) - [j46]Richa Bansal, Sathiakar Collison, Lakshmi Krishnan, Bharat Aggarwal, Mathukumalli Vidyasagar, Siva Teja Kakileti, Geetha Manjunath:
A prospective evaluation of breast thermography enhanced by a novel machine learning technique for screening breast abnormalities in a general population of women presenting to a secondary care hospital. Frontiers Artif. Intell. 5 (2022) - [c69]M. Vidyasagar:
A New Converse Lyapunov Theorem for Global Exponential Stability and Applications to Stochastic Approximation. CDC 2022: 2319-2321 - [i11]M. Vidyasagar:
Convergence of Stochastic Approximation via Martingale and Converse Lyapunov Methods. CoRR abs/2205.01303 (2022) - [i10]Sourav Chatterjee, Mathukumalli Vidyasagar:
Estimating large causal polytree skeletons from small samples. CoRR abs/2209.07028 (2022) - 2021
- [c68]Manindra Agrawal, Madhuri Kanitkar, Mathukumalli Vidyasagar:
SUTRA: An Approach to Modelling Pandemics with Undetected (Asymptomatic) Patients, and Applications to COVID-19. CDC 2021: 3531 - [i9]Rajeeva Laxman Karandikar, M. Vidyasagar:
Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning. CoRR abs/2109.03445 (2021) - 2020
- [j45]Santosh Ansumali, Shaurya Kaushal, Aloke Kumar, Meher K. Prakash, M. Vidyasagar:
Modelling a pandemic with asymptomatic patients, impact of lockdown and herd immunity, with applications to SARS-CoV-2. Annu. Rev. Control. 50: 432-447 (2020) - [j44]Masaaki Nagahara, Debasish Chatterjee, Niharika Challapalli, Mathukumalli Vidyasagar:
CLOT norm minimization for continuous hands-off control. Autom. 113: 108679 (2020) - [j43]Mahsa Lotfi, Mathukumalli Vidyasagar:
Compressed Sensing Using Binary Matrices of Nearly Optimal Dimensions. IEEE Trans. Signal Process. 68: 3008-3021 (2020) - [j42]Shantanu Prasad Burnwal, Mathukumalli Vidyasagar:
Deterministic Completion of Rectangular Matrices Using Asymmetric Ramanujan Graphs: Exact and Stable Recovery. IEEE Trans. Signal Process. 68: 3834-3848 (2020) - [c67]Shashank Ranjan, Mathukumalli Vidyasagar:
For Matrix Recovery, Rank Restricted Isometry Property and Robust Uniform Boundedness Property Imply Rank Robust Null Space Property. ACC 2020: 2194-2196 - [c66]Shantanu Prasad Burnwal, Mathukumalli Vidyasagar:
Exact Completion of Rectangular Matrices Using Ramanujan Bigraphs. ACC 2020: 2203-2206 - [c65]M. Vidyasagar:
Recent Advances in Reinforcement Learning. ACC 2020: 4751-4756 - [c64]Shantanu Prasad Burnwal, Mathukumalli Vidyasagar:
Deterministic Completion of Rectangular Matrices With Measurement Noise Using Unbalanced Ramanujan Bigraphs. CDC 2020: 3477-3480
2010 – 2019
- 2019
- [j41]Murat Arcak, Franco Blanchini, M. Vidyasagar:
Editorial to the Special Issue of L-CSS on Control and Network Theory for Biological Systems. IEEE Control. Syst. Lett. 3(2): 228-229 (2019) - [j40]Mehmet Eren Ahsen, Mathukumalli Vidyasagar:
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory. J. Mach. Learn. Res. 20: 11:1-11:23 (2019) - [j39]Shashank Ranjan, Mathukumalli Vidyasagar:
Tight Performance Bounds for Compressed Sensing With Conventional and Group Sparsity. IEEE Trans. Signal Process. 67(11): 2854-2867 (2019) - [c63]Shantanu Prasad Burnwal, Mathukumalli Vidyasagar:
Construction of High-Degree Ramanujan Graphs With Applications to Matrix Completion. ACC 2019: 4814-4817 - [c62]Shashank Ranjan, Mathukumalli Vidyasagar:
Deterministic Construction of Bipolar Matrices For Compressed Sensing. CDC 2019: 3661-3663 - [c61]Shantanu Prasad Burnwal, Mathukumalli Vidyasagar:
Completion of Rectangular Matrices Using Asymmetric Ramanujan Graphs. CDC 2019: 6487-6490 - [i8]Shantanu Prasad Burnwal, Mathukumalli Vidyasagar:
Deterministic Completion of Rectangular Matrices Using Asymmetric Ramanujan Graphs. CoRR abs/1908.00963 (2019) - [i7]Shantanu Prasad Burnwal, Mathukumalli Vidyasagar, Kaneenika Sinha:
Deterministic Completion of Rectangular Matrices Using Ramanujan Bigraphs - II: Explicit Constructions and Phase Transitions. CoRR abs/1910.03937 (2019) - 2018
- [j38]Nitin K. Singh, Mehmet Eren Ahsen, Niharika Challapalli, Hyun-Seok Kim, Michael A. White, Mathukumalli Vidyasagar:
Inferring Genome-Wide Interaction Networks Using the Phi-Mixing Coefficient, and Applications to Lung and Breast Cancer. IEEE Trans. Mol. Biol. Multi Scale Commun. 4(3): 123-139 (2018) - [j37]Mahsa Lotfi, Mathukumalli Vidyasagar:
A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices. IEEE Trans. Signal Process. 66(15): 4079-4089 (2018) - [c60]Mahsa Lotfi, Mathukumalli Vidyasagar:
Array LDPC Code-based Compressive Sensing. Allerton 2018: 682-685 - [i6]Mahsa Lotfi, Mathukumalli Vidyasagar:
Compressed Sensing Using Binary Matrices of Nearly Optimal Dimensions. CoRR abs/1808.03001 (2018) - 2017
- [j36]Mathukumalli Vidyasagar:
Machine learning methods in computational cancer biology. Annu. Rev. Control. 43: 107-127 (2017) - [j35]Mehmet Eren Ahsen, Niharika Challapalli, Mathukumalli Vidyasagar:
Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect. J. Mach. Learn. Res. 18: 54:1-54:24 (2017) - [c59]Mahsa Lotfi, Mathukumalli Vidyasagar:
A fast single-pass algorithm for compressive sensing based on binary measurement matrices. Allerton 2017: 369-373 - [c58]Hema Kumari Achanta, Burook Misganaw, Mathukumalli Vidyasagar:
A multi-view ℓ1-norm SVM algorithm for data integration in biological applications. ACC 2017: 3753-3757 - [c57]Hema Kumari Achanta, Burook Misganaw, M. Vidyasagar:
Integrating biological data across multiple platforms using importance-weighted transfer learning and applications to breast cancer data sets. CCTA 2017: 955-960 - [c56]Mahsa Lotfi, Burook Misganaw, Mathukumalli Vidyasagar:
Prediction of Time to Tumor Recurrence in Ovarian Cancer: Comparison of Three Sparse Regression Methods. ISBRA 2017: 1-11 - [i5]Mahsa Lotfi, Mathukumalli Vidyasagar:
A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices. CoRR abs/1708.03608 (2017) - [i4]Niharika Challapalli, Masaaki Nagahara, Mathukumalli Vidyasagar:
CLOT Norm Minimization for Continuous Hands-off Control. CoRR abs/1710.07952 (2017) - 2016
- [j34]Nitin K. Singh, Mathukumalli Vidyasagar:
bLARS: An Algorithm to Infer Gene Regulatory Networks. IEEE ACM Trans. Comput. Biol. Bioinform. 13(2): 301-314 (2016) - [c55]Burook Misganaw, Mathukumalli Vidyasagar:
A robust platform-independent gene signature for single-sample breast cancer subtyping. ACC 2016: 1301-1304 - [c54]Hema Kumari Achanta, Burook Misganaw, M. Vidyasagar:
A transfer learning approach for integrating biological data across platforms. ACC 2016: 6695-6697 - [c53]Mehmet Eren Ahsen, Todd P. Boren, Nitin K. Singh, Burook Misganaw, Jayanthi S. Lea, David S. Miller, Michael A. White, Mathukumalli Vidyasagar:
Sparse Feature Selection for Classification and Prediction of Metastasis in Endometrial Cancer: Extended Abstract. BCB 2016: 522-524 - [c52]Niharika Challapalli, Mehmet Eren Ahsen, Mathukumalli Vidyasagar:
Modelling drug response and resistance in cancer: Opportunities and challenges. CDC 2016: 2488-2493 - [c51]M. Vidyasagar:
A tutorial introduction to compressed sensing. CDC 2016: 5091-5104 - [i3]Niharika Challapalli, Masaaki Nagahara, Mathukumalli Vidyasagar:
Continuous Hands-off Control by CLOT Norm Minimization. CoRR abs/1611.02071 (2016) - 2015
- [c50]Mehmet Eren Ahsen, Mathukumalli Vidyasagar:
A PAC learning approach to one-bit compressed sensing. ACC 2015: 4228-4230 - [c49]Burook Misganaw, Mehmet Eren Ahsen, Nitin K. Singh, Keith A. Baggerly, Anna Unruh, Michael A. White, Mathukumalli Vidyasagar:
Optimized prediction of extreme treatment outcomes in ovarian cancer. CDC 2015: 1254-1258 - [c48]Mehmet Eren Ahsen, Mathukumalli Vidyasagar:
An approach to one-bit compressed sensing based on probably approximately correct learning theory. CDC 2015: 7377-7379 - [r2]Mathukumalli Vidyasagar:
Learning Theory. Encyclopedia of Systems and Control 2015 - 2014
- [j33]Mehmet Eren Ahsen, Mathukumalli Vidyasagar:
Mixing Coefficients Between Discrete and Real Random Variables: Computation and Properties. IEEE Trans. Autom. Control. 59(1): 34-47 (2014) - [c47]Mehmet Eren Ahsen, Mathukumalli Vidyasagar:
Near-ideal behavior of compressed sensing algorithms. CDC 2014: 6354-6357 - [c46]Nitin K. Singh, M. Vidyasagar:
Inferring Gene Regulatory Networks with Sparse Bayesian Learning and phi-mixing coefficient. ECC 2014: 1510-1515 - [c45]Mehmet Eren Ahsen, Mathukumalli Vidyasagar:
Near-ideal behavior of some compressed sensing algorithms. ECC 2014: 2216-2218 - [i2]Mathukumalli Vidyasagar:
Machine Learning Methods in the Computational Biology of Cancer. CoRR abs/1402.5728 (2014) - 2013
- [c44]Mehmet Eren Ahsen, M. Vidyasagar:
On the computation of mixing coefficients between discrete-valued random variables. ASCC 2013: 1-5 - [c43]Mathukumalli Vidyasagar:
Near-ideal behavior of a modified Elastic Net algorithm. CDC 2013: 6011-6012 - [c42]Nitin K. Singh, Aishwarya Sundaresan, Mathukumalli Vidyasagar:
A sparse Bayesian learning based approach to Inferring Gene Regulatory Networks. GlobalSIP 2013: 113-116 - 2012
- [b3]Mathukumalli Vidyasagar:
Computational Cancer Biology - An Interaction Network Approach. Springer Briefs in Electrical and Computer Engineering, Springer 2012, ISBN 978-1-4471-4750-3, pp. i-xii, 1-80 - [j32]Mathukumalli Vidyasagar:
A Metric Between Probability Distributions on Finite Sets of Different Cardinalities and Applications to Order Reduction. IEEE Trans. Autom. Control. 57(10): 2464-2477 (2012) - [c41]Nitin K. Singh, Mehmet Eren Ahsen, Shiva Mankala, M. Vidyasagar, Michael A. White:
A novel application of mixing coefficients for reverse-engineering gene interaction networks. Allerton Conference 2012: 1461-1466 - [c40]Mathukumalli Vidyasagar, Yutaka Yamamoto:
Convergence and compactness of families of proper plants in the graph topology. CDC 2012: 232-239 - [c39]Mehmet Eren Ahsen, Nitin K. Singh, Todd P. Boren, M. Vidyasagar, Michael A. White:
A new feature selection algorithm for two-class classification problems and application to endometrial cancer. CDC 2012: 2976-2982 - [c38]Nitin K. Singh, Mehmet Eren Ahsen, Shiva Mankala, M. Vidyasagar, Michael A. White:
Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient. GENSiPS 2012: 168-171 - 2011
- [b2]Mathukumalli Vidyasagar:
Control System Synthesis: A Factorization Approach, Part I. Synthesis Lectures on Control and Mechatronics, Morgan & Claypool Publishers 2011, ISBN 9781608456628, pp. 1-184 - [b1]Mathukumalli Vidyasagar:
Control System Synthesis: A Factorization Approach, Part II. Synthesis Lectures on Control and Mechatronics, Morgan & Claypool Publishers 2011, ISBN 9781608456635, pp. 1-227 - [j31]Mathukumalli Vidyasagar:
Probabilistic Methods in Cancer Biology. Eur. J. Control 17(5-6): 483-511 (2011) - [j30]M. Vidyasagar:
The complete realization problem for hidden Markov models: a survey and some new results. Math. Control. Signals Syst. 23(1-3): 1-65 (2011) - [c37]Yutaka Yamamoto, Mathukumalli Vidyasagar:
Compact sets in the graph topology and applications to approximation of system design. CDC/ECC 2011: 621-626 - [c36]M. Vidyasagar:
A metric between probability distributions on finite sets of different cardinalities. CDC/ECC 2011: 710-715 - [c35]M. Vidyasagar:
Optimal order reduction of probability distributions by maximizing mutual information. CDC/ECC 2011: 716-721 - [c34]Kun Deng, Prashant G. Mehta, Sean P. Meyn, Mathukumalli Vidyasagar:
A recursive learning algorithm for model reduction of Hidden Markov Models. CDC/ECC 2011: 4674-4679 - [i1]Mathukumalli Vidyasagar:
Metrics Between Probability Distributions on Finite Sets of Different Cardinalities by Maximizing Mutual Information (MMI). CoRR abs/1104.4521 (2011) - 2010
- [c33]Mathukumalli Vidyasagar:
Kullback-Leibler divergence rate between probability distributions on sets of different cardinalities. CDC 2010: 948-953 - [c32]Mathukumalli Vidyasagar:
Reduced-order modeling of Markov and hidden Markov processes via aggregation. CDC 2010: 1810-1815
2000 – 2009
- 2009
- [c31]Mathukumalli Vidyasagar:
An elementary derivation of the large deviation rate function for finite state Markov chains. CDC 2009: 1599-1606 - [r1]M. Vidyasagar:
Learning, System Identification, and Complexity. Encyclopedia of Complexity and Systems Science 2009: 5206-5218 - 2008
- [j29]Mustafa Khammash, Claire J. Tomlin, M. Vidyasagar:
Guest Editorial - Special Issue on Systems Biology. IEEE Trans. Autom. Control. 53: 4-7 (2008) - [j28]Mathukumalli Vidyasagar, Sharmila S. Mande, Ch. V. Siva K. Reddy, V. V. Raja Rao:
The 4M (Mixed Memory Markov Model) Algorithm for Finding Genes in Prokaryotic Genomes. IEEE Trans. Autom. Control. 53: 26-37 (2008) - 2007
- [c30]Kshama Goyal, M. Vidyasagar:
Predicting protein-protein interactions in E. coli using machine learning methods. CDC 2007: 4539-4544 - [c29]M. Vidyasagar:
Bounds on the kullback-leibler divergence rate between hidden markov models. CDC 2007: 6160-6165 - [c28]Dipti Deodhare, M. Vidyasagar, M. Narasimha Murty:
A Unified Approach to Encoding and Classification Using Bimodal Projection-Based Features. ICCTA 2007: 348-354 - 2006
- [c27]M. Vidyasagar:
The 4M (Mixed Memory Markov Model) Algorithm for Finding Genes from Prokaryote Genomes. CDC 2006: 1-6 - [c26]Dipti Deodhare, M. Vidyasagar, M. Narasimha Murty:
Bimodal Projection-based Features for Pattern Classification. IJCNN 2006: 4719-4726 - 2005
- [c25]M. Vidyasagar:
Convergence of Empirical Means with Alpha-Mixing Input Sequences, and an Application to PAC Learning. CDC/ECC 2005: 560-565 - [c24]M. Vidyasagar:
The Realization Problem for Hidden Markov Models: The Complete Realization Problem. CDC/ECC 2005: 6632-6637 - 2003
- [c23]M. Vidyasagar:
Some challenges in computational biology. ECC 2003: 3364-3369 - [c22]M. Vidyasagar:
Synthesis of hidden Markov models based on finite sample paths and applications to computational biology. ECC 2003: 3392-3396 - 2001
- [j27]M. Vidyasagar, Vincent D. Blondel:
Probabilistic solutions to some NP-hard matrix problems. Autom. 37(9): 1397-1405 (2001) - [j26]M. Vidyasagar:
Randomized algorithms for robust controller synthesis using statistical learning theory. Autom. 37(10): 1515-1528 (2001) - [j25]Mathukumalli Vidyasagar:
Randomized Algorithms for Robust Controller Synthesis Using Statistical Learning Theory: A Tutorial Overview. Eur. J. Control 7(2-3): 287-310 (2001) - [j24]Marco C. Campi, Mathukumalli Vidyasagar:
Learning with prior information. IEEE Trans. Autom. Control. 46(11): 1682-1695 (2001) - [c21]Mathukumalli Vidyasagar, Rajeeva Laxman Karandikar:
System identification: a learning theory approach. CDC 2001: 2001-2006 - 2000
- [j23]M. Vidyasagar, Sanjeev R. Kulkarni:
Some contributions to fixed-distribution learning theory. IEEE Trans. Autom. Control. 45(2): 217-234 (2000) - [c20]Marco C. Campi, M. Vidyasagar:
Learning with prior information. ISCAS 2000: 255-258
1990 – 1999
- 1998
- [j22]Dipti Deodhare, M. Vidyasagar, S. Sathiya Keerthi:
Synthesis of fault-tolerant feedforward neural networks using minimax optimization. IEEE Trans. Neural Networks 9(5): 891-900 (1998) - 1997
- [j21]Sanjeev R. Kulkarni, M. Vidyasagar:
Learning decision rules for pattern classification under a family of probability measures. IEEE Trans. Inf. Theory 43(1): 154-166 (1997) - 1996
- [j20]M. Vidyasagar:
A Brief History of the Graph Topology*. Eur. J. Control 2(2): 80-87 (1996) - 1995
- [j19]Mathukumalli Vidyasagar:
Minimum-seeking properties of analog neural networks with multilinear objective functions. IEEE Trans. Autom. Control. 40(8): 1359-1375 (1995) - 1993
- [j18]Mathukumalli Vidyasagar:
Location and stability of the high-gain equilibria of nonlinear neural networks. IEEE Trans. Neural Networks 4(4): 660-672 (1993) - [c19]M. Vidyasagar:
Convergence of higher-order neural networks with modified updating. ICNN 1993: 1379-1384 - 1992
- [j17]David Wang, Mathukumalli Vidyasagar:
Modeling a class of multilink manipulators with the last link flexible. IEEE Trans. Robotics Autom. 8(1): 33-41 (1992) - [c18]Ajay Gopalswamy, Pramod Gupta, M. Vidyasagar:
A new parallelogram linkage configuration for gravity compensation using torsional springs. ICRA 1992: 664-669 - 1991
- [j16]David Wang, Mathukumalli Vidyasagar:
Transfer Functions for a Single Flexible Link. Int. J. Robotics Res. 10(5): 540-549 (1991) - [c17]David Wang, Mathukumalli Vidyasagar:
Observer-controller stabilization of a class of manipulators with a single flexible link. ICRA 1991: 516-521 - [c16]A. Sankaranarayanan, M. Vidyasagar:
Path planning for moving a point object amidst unknown obstacles in a plane: the universal lower bound on the worst path lengths and a classification of algorithms. ICRA 1991: 1734-1741 - [c15]David Vinke, M. Vidyasagar:
New techniques for H2 optimal control of a flexible beam. ICRA 1991: 2592-2597 - [c14]Hemanshu Roy Pota, M. Vidyasagar:
Passivity of flexible beam transfer functions with modified outputs. ICRA 1991: 2826-2831 - 1990
- [j15]Yao-Chon Chen, Mathukumalli Vidyasagar:
Optimal control of robotic manipulators in the presence of obstacles. J. Field Robotics 7(5): 721-740 (1990) - [c13]David Wang, M. Vidyasagar:
Passive control of a single flexible link. ICRA 1990: 1432-1437 - [c12]A. Sankaranarayanan, M. Vidyasagar:
A new path planning algorithm for moving a point object amidst unknown obstacles in a plane. ICRA 1990: 1930-1936 - [c11]M. Vidyasagar:
Improved neural networks for analog to digital conversion. IJCNN 1990: 517-522 - [c10]M. Vidyasagar:
An analysis of the flows of neural networks with linear interconnections. IJCNN 1990: 523-528
1980 – 1989
- 1989
- [c9]David Wang, M. Vidyasagar:
Transfer functions for a single flexible link. ICRA 1989: 1042-1047 - 1988
- [c8]H. Krishnan, Mathukumalli Vidyasagar:
Control of a single-link flexible beam using a Hankel-norm-based reduced order model. ICRA 1988: 9-14 - [c7]David Wang, Mathukumalli Vidyasagar:
Modelling of a 5-bar-linkage manipulator with one flexible link. ICRA 1988: 21-26 - [c6]Yao-Chon Chen, Mathukumalli Vidyasagar:
Optimal trajectory planning for planar n-link revolute manipulators in the presence of obstacles. ICRA 1988: 202-208 - 1987
- [j14]M. Vidyasagar:
Some results on simultaneous stabilization with multiple domains of stability. Autom. 23(4): 535-540 (1987) - [j13]C. C. H. Ma, M. Vidyasagar:
Parametric conditions for stability of reduced-order linear time-varying control systems. Autom. 23(5): 625-634 (1987) - [j12]Mark W. Spong, Mathukumalli Vidyasagar:
Robust linear compensator design for nonlinear robotic control. IEEE J. Robotics Autom. 3(4): 345-351 (1987) - [c5]I. Y. Shung, M. Vidyasagar:
Control of a flexible robot arm with bounded input: Optimum step responses. ICRA 1987: 916-922 - [c4]David Wang, M. Vidyasagar:
Control of a flexible beam for optimum step response. ICRA 1987: 1567-1572 - [c3]Y. C. Chen, M. Vidyasagar:
Some qualitative results on the collision-free joint space of a planar n-DOF linkage. ICRA 1987: 1623-1630 - 1986
- [j11]M. Vidyasagar, H. Kimura:
Robust controllers for uncertain linear multivariable systems. Autom. 22(1): 85-94 (1986) - [j10]H. Maeda, M. Vidyasagar:
Infinite gain margin problem in multivariable feedback systems. Autom. 22(1): 131-133 (1986) - [j9]Mathukumalli Vidyasagar:
New directions of research in nonlinear system theory. Proc. IEEE 74(8): 1060-1091 (1986) - [c2]M. Vidyasagar, Y. Xiong:
Control of a flexible robot arm using the stable factorization approach. ICRA 1986: 1037 - 1985
- [j8]Anthony Vannelli, M. Vidyasagar:
Maximal lyapunov functions and domains of attraction for autonomous nonlinear systems. Autom. 21(1): 69-80 (1985) - [j7]M. Vidyasagar, N. Viswanadham:
Reliable stabilization using a multi-controller configuration. Autom. 21(5): 599-602 (1985) - [j6]Jake K. Aggarwal, Mathukumalli Vidyasagar:
Nonlinear systems: Stability analysis. IEEE Trans. Syst. Man Cybern. 15(4): 596-597 (1985) - [c1]Mark W. Spong, Mathukumalli Vidyasagar:
Robust linear compensator design for nonlinear robotic control. ICRA 1985: 954-959 - 1983
- [j5]Bruce A. Francis, M. Vidyasagar:
Algebraic and topological aspects of the regulator problem for lumped linear systems. Autom. 19(1): 87-90 (1983) - 1980
- [j4]Mathukumalli Vidyasagar:
Review of "Qualitative Analysis of Large Scale Dynamical Systems" by Anthony N. Michel and Richard K. Miller. IEEE Trans. Syst. Man Cybern. 10(10): 689 (1980)
1970 – 1979
- 1977
- [j3]S. R. K. Dutta, Mathukumalli Vidyasagar:
New algorithms for constrained minimax optimization. Math. Program. 13(1): 140-155 (1977) - 1972
- [j2]Mathukumalli Vidyasagar:
Review of "The Analysis of Feedback Systems" by Jan C. Willems. IEEE Trans. Syst. Man Cybern. 2(4): 559-560 (1972) - [j1]Mathukumalli Vidyasagar:
Review of "Nonlinear System Theory-A Functional Analysis Approach" by Jack M. Holtzman. IEEE Trans. Syst. Man Cybern. 2(4): 560 (1972)
Coauthor Index
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