Fetching the paper…
Reading the bibliography…
Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to their linear counterparts.
Deep learning recommendation model for personalization and recommendation systems
Naumov, M., Mudigere, D., Shi, H. M., Huang, J., Sundaraman, N., Park, J., Wang, X., Gupta, U., Wu, C., Azzolini, A. G., Dzhulgakov, D., Mallevich, A., Cherniavskii, I., Lu, Y., Krishnamoorthi, R., Yu, A., Kondratenko, V., Pereira, S., Chen, X., Chen, W., Rao, V., Jia, B., Xiong, L., and Smelyanskiy, M · 1906
Earlier work this paper cites.
On a routing problem
Bellman, R · 1958
Earlier work this paper cites.
An analysis of the greedy heuristic for independence systems
Korte, B. and Hausmann, D · 1978
Earlier work this paper cites.
On a greedy heuristic for complete matching
Reingold, E. M. and Tarjan, R. E · 1981
Earlier work this paper cites.
An analysis of the greedy algorithm for the submodular set covering problem
Wolsey, L. A · 1982
Earlier work this paper cites.
Linear programming
Chvatal, V., Chvatal, V., et al · 1983
Earlier work this paper cites.
Discriminatory analysis: nonparametric discrimination, consistency properties , volume 1
Fix, E · 1985
Earlier work this paper cites.
Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets
Faloutsos, C. and Lin, K.-I · 1995
Earlier work this paper cites.
A reinforcement learning approach to job-shop scheduling
Zhang, W. and Dietterich, T. G · 1995
Earlier work this paper cites.
Arriving on time
Fan, Y., Kalaba, R. E., and Moore, J. E · 2005
Earlier work this paper cites.
Stochastic shortest paths via quasi-convex maximization
Nikolova, E., Kelner, J. A., Brand, M., and Mitzenmacher, M · 2006
Earlier work this paper cites.
Mixed integer programming
Wolsey, L. A · 2007
Earlier work this paper cites.
Scip: solving constraint integer programs
Achterberg, T · 2009
Earlier work this paper cites.
Python 3 Reference Manual
Van Rossum, G. and Drake, F. L · 2009
Earlier work this paper cites.
An automatic method for solving discrete programming problems
Land, A. H. and Doig, A. G · 2010
Earlier work this paper cites.
Non-convex mixed-integer nonlinear programming: A survey
Burer, S. and Letchford, A. N · 2012
Earlier work this paper cites.
Meta-heuristics: Advances and trends in local search paradigms for optimization
Voß, S., Martello, S., Osman, I. H., and Roucairol, C · 2012
Earlier work this paper cites.
Mixed-integer nonlinear optimization
Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., Luedtke, J., and Mahajan, A · 2013
Earlier work this paper cites.
Practical route planning under delay uncertainty: Stochastic shortest path queries
Lim, S., Sommer, C., Nikolova, E., and Rus, D · 2013
Earlier work this paper cites.
Evolutionary optimization algorithms
Simon, D · 2013
Earlier work this paper cites.
Multi-task bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P · 2013
Earlier work this paper cites.
Learning surrogate models for simulation-based optimization
Cozad, A., Sahinidis, N. V., and Miller, D. C · 2014
Earlier work this paper cites.
Simulation-based optimization
Gosavi, A. et al · 2015
Earlier work this paper cites.
Derivative-free methods for mixed-integer constrained optimization problems
Liuzzi, G., Lucidi, S., and Rinaldi, F · 2015
Earlier work this paper cites.
Taking the human out of the loop: A review of bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and de Freitas, N · 2015
Earlier work this paper cites.
Learning to branch in mixed integer programming
Khalil, E., Le Bodic, P., Song, L., Nemhauser, G., and Dilkina, B · 2016
Earlier work this paper cites.
An overview of gradient descent optimization algorithms
Ruder, S · 2016
Earlier work this paper cites.
Optnet: Differentiable optimization as a layer in neural networks
Amos, B. and Kolter, J. Z · 2017
Earlier work this paper cites.
Differentiable learning of submodular models
Djolonga, J. and Krause, A · 2017
Cited alongside, same era.
Task-based end-to-end model learning in stochastic optimization
Donti, P., Amos, B., and Kolter, J. Z · 2017
Cited alongside, same era.
Learning combinatorial optimization algorithms over graphs
Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Bayesian optimization of combinatorial structures
Baptista, R. and Poloczek, M · 2018
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
Cited alongside, same era.
Completing density functional theory by machine learning hidden messages from molecules
Nagai, R., Akashi, R., and Sugino, O · 2020
Later among the works it cites.
Solving mixed integer programs using neural networks
Nair, V., Bartunov, S., Gimeno, F., Von Glehn, I., Lichocki, P., Lobov, I., O’Donoghue, B., Sonnerat, N., Tjandraatmadja, C., Wang, P., et al · 2020
Later among the works it cites.
Inference in artificial intelligence with deep optics and photonics
Wetzstein, G., Ozcan, A., Gigan, S., Fan, S., Englund, D., Soljačić, M., Denz, C., Miller, D. A., and Psaltis, D · 2020
Later among the works it cites.
Transferable graph optimizers for ml compilers
Zhou, Y., Roy, S., Abdolrashidi, A., Wong, D., Ma, P., Xu, Q., Liu, H., Phothilimtha, P., Wang, S., Goldie, A., et al · 2020
Later among the works it cites.
Quantum circuits with many photons on a programmable nanophotonic chip
Arrazola, J. M., Bergholm, V., Brádler, K., Bromley, T. R., Collins, M. J., Dhand, I., Fumagalli, A., Gerrits, T., Goussev, A., Helt, L. G., et al · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The fastmap algorithm for shortest path computations
Cohen, L., Uras, T., Jahangiri, S., Arunasalam, A., Koenig, S., and Kumar, T. S · 2018
Cited alongside, same era.
Scalable meta-learning for bayesian optimization
Feurer, M., Letham, B., and Bakshy, E · 2018
Cited alongside, same era.
Attention, learn to solve routing problems!
Kool, W., van Hoof, H., and Welling, M · 2018
Cited alongside, same era.
Combinatorial optimization with graph convolutional networks and guided tree search
Li, Z., Chen, Q., and Koltun, V · 2018
Cited alongside, same era.
Reinforcement learning for solving the vehicle routing problem
Nazari, M., Oroojlooy, A., Snyder, L., and Takác, M · 2018
Cited alongside, same era.
Nevergrad - A gradient-free optimization platform
Rapin, J. and Teytaud, O · 2018
Cited alongside, same era.
Later among the works it cites.
Machine learning for combinatorial optimization: a methodological tour d’horizon
Bengio, Y., Lodi, A., and Prouvost, A · 2021
Later among the works it cites.
Mercer features for efficient combinatorial bayesian optimization
Deshwal, A., Belakaria, S., and Doppa, J. R · 2021
Later among the works it cites.
DC3: A learning method for optimization with hard constraints
Donti, P. L., Rolnick, D., and Kolter, J. Z · 2021
Later among the works it cites.
Pygad: An intuitive genetic algorithm python library, 2021
Gad, A. F · 2021
Later among the works it cites.
Highly accurate protein structure prediction with alphafold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al · 2021
Later among the works it cites.
Accurate modeling of antenna structures by means of domain confinement and pyramidal deep neural networks
Koziel, S., Çalık, N., Mahouti, P., and Belen, M. A · 2021
Later among the works it cites.
Learning to delegate for large-scale vehicle routing
Li, S., Yan, Z., and Wu, C · 2021
Later among the works it cites.
Risk bounds and calibration for a smart predict-then-optimize method
Liu, H. and Grigas, P · 2021
Later among the works it cites.
Reinforcement learning for combinatorial optimization: A survey
Mazyavkina, N., Sviridov, S., Ivanov, S., and Burnaev, E · 2021
Later among the works it cites.
A graph placement methodology for fast chip design
Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J. W., Songhori, E., Wang, S., Lee, Y.-J., Johnson, E., Pathak, O., Nazi, A., et al · 2021
Later among the works it cites.
Implicit mle: backpropagating through discrete exponential family distributions
Niepert, M., Minervini, P., and Franceschi, L · 2021
Later among the works it cites.
Value learning for throughput optimization of deep learning workloads
Steiner, B., Cummins, C., He, H., and Leather, H · 2021
Later among the works it cites.
Learning space partitions for path planning
Yang, K., Zhang, T., Cummins, C., Cui, B., Steiner, B., Wang, L., Gonzalez, J. E., Klein, D., and Tian, Y · 2021
Later among the works it cites.
Scalable first-order bayesian optimization via structured automatic differentiation
Ament, S. E. and Gomes, C. P · 2022
Closest in time.
A divide and conquer algorithm for predict+ optimize with non-convex problems
Guler, A. U., Demirović, E., Chan, J., Bailey, J., Leckie, C., and Stuckey, P. J · 2022
Closest in time.
Gurobi Optimizer Reference Manual, 2022
Gurobi Optimization, LLC · 2022
Closest in time.
Fast rates for contextual linear optimization
Hu, Y., Kallus, N., and Mao, X · 2022
Closest in time.
Constrained discrete black-box optimization using mixed-integer programming
Papalexopoulos, T. P., Tjandraatmadja, C., Anderson, R., Vielma, J. P., and Belanger, D · 2022
Closest in time.
Theseus: A library for differentiable nonlinear optimization
Pineda, L., Fan, T., Monge, M., Venkataraman, S., Sodhi, P., Chen, R. T., Ortiz, J., DeTone, D., Wang, A., Anderson, S., et al · 2022
Closest in time.
Inverse design of photonic devices with strict foundry fabrication constraints
Schubert, M. F., Cheung, A. K. C., Williamson, I. A. D., Spyra, A., and Alexander, D. H · 2022
Closest in time.
Recshard: statistical feature-based memory optimization for industry-scale neural recommendation
Sethi, G., Acun, B., Agarwal, N., Kozyrakis, C., Trippel, C., and Wu, C.-J · 2022
Closest in time.
Multi-objective optimization by learning space partition
Zhao, Y., Wang, L., Yang, K., Zhang, T., Guo, T., and Tian, Y · 2022
Closest in time.
Pre-train and search: Efficient embedding table sharding with pre-trained neural cost models
Zha, D., Feng, L., Luo, L., Bhushanam, B., Liu, Z., Hu, Y., Nie, J., Huang, Y., Tian, Y., Kejariwal, A., et al · 2023
Closest in time.