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Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, etc.
A quantum interior-point predictor–corrector algorithm for linear programming
Casares, P. A. M. and Martin-Delgado, M. A · 1902
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Distributional property testing in a quantum world
Gilyén, A. and Li, T · 1902
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Quantum lower bounds by quantum arguments
Ambainis, A · 2000
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An optimal algorithm for Monte Carlo estimation
Dagum, P., Karp, R., Luby, M., and Ross, S · 2000
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Quantum computation and quantum information
Nielsen, M. A. and Chuang, I. L · 2000
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Quantum amplitude amplification and estimation
Brassard, G., Hoyer, P., Mosca, M., and Tapp, A · 2002
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Solving large scale linear prediction problems using stochastic gradient descent algorithms
Zhang, T · 2004
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On the power of ambainis lower bounds
Zhang, S · 2005
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Polynomial degree vs. quantum query complexity
Ambainis, A · 2006
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Quantum algorithms for escaping from saddle points
Zhang, C., Leng, J., and Li, T · 2007
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No quantum speedup over gradient descent for non-smooth convex optimization
Garg, A., Kothari, R., Netrapalli, P., and Sherif, S · 2010
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Reconstructing strings from substrings with quantum queries
Cleve, R., Iwama, K., Le Gall, F., Nishimura, H., Tani, S., Teruyama, J., and Yamashita, S · 2012
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A stochastic gradient method with an exponential convergence rate for finite training sets
Roux, N., Schmidt, M., and Bach, F · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
Johnson, R. and Zhang, T · 2013
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Stochastic dual coordinate ascent methods for regularized loss minimization
Shalev-Shwartz, S. and Zhang, T · 2013
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Linear convergence with condition number independent access of full gradients
Zhang, L., Mahdavi, M., and Jin, R · 2013
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Quantum algorithms for search with wildcards and combinatorial group testing
Ambainis, A. and Montanaro, A · 2014
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Saga: A fast incremental gradient method with support for non-strongly convex composite objectives
Defazio, A., Bach, F., and Lacoste-Julien, S · 2014
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Quantum support vector machine for big data classification
Rebentrost, P., Mohseni, M., and Lloyd, S · 2014
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A proximal stochastic gradient method with progressive variance reduction
Xiao, L. and Zhang, T · 2014
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Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization
Blanchet, J. H. and Glynn, P. W · 2015
Cited alongside, same era.
Quantum speedup of Monte Carlo methods
Montanaro, A · 2015
Cited alongside, same era.
Improved SVRG for non-strongly-convex or sum-of-non-convex objectives
Allen-Zhu, Z. and Yuan, Y · 2016
Cited alongside, same era.
Stochastic variance reduction for nonconvex optimization
Reddi, S. J., Hefny, A., Sra, S., Poczos, B., and Smola, A · 2016
Cited alongside, same era.
SDCA without duality, regularization, and individual convexity
Shalev-Shwartz, S · 2016
Quantum algorithms and lower bounds for convex optimization
Chakrabarti, S., Childs, A. M., Li, T., and Wu, X · 2020
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A quantum interior point method for LPs and SDPs
Kerenidis, I. and Prakash, A · 2020
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Stochastic bias-reduced gradient methods
Asi, H., Carmon, Y., Jambulapati, A., Jin, Y., and Sidford, A · 2021
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Near-optimal lower bounds for convex optimization for all orders of smoothness
Garg, A., Kothari, R., Netrapalli, P., and Sherif, S · 2021
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Cited alongside, same era.
Tight complexity bounds for optimizing composite objectives
Woodworth, B. E. and Srebro, N · 2016
Cited alongside, same era.
Katyusha: The first direct acceleration of stochastic gradient methods
Allen-Zhu, Z · 2017
Cited alongside, same era.
Quantum speed-ups for semidefinite programming
Brandão, F. G. and Svore, K · 2017
Cited alongside, same era.
Fast quantum algorithms for least squares regression and statistic leverage scores
Liu, Y. and Zhang, S · 2017
Cited alongside, same era.
Quantum SDP-solvers: Better upper and lower bounds
van Apeldoorn, J., Gilyén, A., Gribling, S., and de Wolf, R · 2017
Cited alongside, same era.
Quantum algorithm for linear regression
Wang, G · 2017
Cited alongside, same era.
Katyusha X: Practical momentum method for stochastic sum-of-nonconvex optimization, 2018
Allen-Zhu, Z · 2018
Cited alongside, same era.
Hamoudi, Y · 2021
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Quantum simulation of real-space dynamics
Childs, A. M., Leng, J., Li, T., Liu, J.-P., and Zhang, C · 2022
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Near-optimal quantum algorithms for multivariate mean estimation
Cornelissen, A., Hamoudi, Y., and Jerbi, S · 2022
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Robustness of quantum algorithms for nonconvex optimization, 2022
Gong, W., Zhang, C., and Li, T · 2022
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Li, T. and Zhang, R · 2022
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Quantum lower bounds for finding stationary points of nonconvex functions, 2022
Zhang, C. and Li, T · 2022
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Quantum algorithms and lower bounds for linear regression with norm constraints
Chen, Y. and de Wolf, R · 2023
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Quantum Langevin dynamics for optimization, 2023
Chen, Z., Lu, Y., Wang, H., Liu, Y., and Li, T · 2023
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A sublinear-time quantum algorithm for approximating partition functions
Cornelissen, A. and Hamoudi, Y · 2023
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Mean estimation when you have the source code; or, quantum Monte Carlo methods
Kothari, R. and O’Donnell, R · 2023
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Quantum Hamiltonian descent, 2023
Leng, J., Hickman, E., Li, J., and Wu, X · 2023
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On quantum speedups for nonconvex optimization via quantum tunneling walks
Liu, Y., Su, W. J., and Li, T · 2023
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Ozgul, G., Li, X., Mahdavi, M., and Wang, C · 2023
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Shao, C · 2023
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Quantum speedups for stochastic optimization
Sidford, A. and Zhang, C · 2023
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Quantum lower bounds for finding stationary points of nonconvex functions
Zhang, C. and Li, T · 2023
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