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Generating Design Suggestions under Tight Constraints with Gradient-based Probabilistic Programming

Daniel Ritchie, Sharon Lin, Noah D. Goodman, Pat Hanrahan
2015 Computer graphics forum (Print)  
We implement HMC in a high-performance probabilistic programming language, and we evaluate its ability to efficiently generate suggestions for two different, highly-constrained example applications: vector  ...  Abstract We present a system for generating suggestions from highly-constrained, continuous design spaces.  ...  This material is based on research sponsored by DARPA under agreement number FA8750-14-2-0009. The U.S.  ... 
doi:10.1111/cgf.12580 fatcat:3tloy2zj55cf5bpriqbnbrrfbm

Robust PID design by chance-constrained optimization

Pedro Mercader, Kristian Soltesz, Alfonso Baños
2017 Journal of the Franklin Institute  
., maximization of integral gain under constraints on the H∞-norm of relevant closed-loop transfer functions.  ...  A method for synthesizing proportional-integral-derivative (PID) controllers for process models with probabilistic parametric uncertainty is presented.  ...  Acknowledgment This work has been supported by Ministerio de Economía e Innovación of Spain under project DPI2013-47100-C2-1-P (including FEDER co-funding).  ... 
doi:10.1016/j.jfranklin.2017.10.017 fatcat:x346sngsknb5xeqfkp6gxciyku

Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations [article]

Glen Chou, Hao Wang, Dmitry Berenson
2022 arXiv   pre-print
We then train a GP representation of the constraint which is consistent with and which generalizes this information.  ...  Our approach uses the Karush-Kuhn-Tucker (KKT) optimality conditions to determine where on the demonstrations the constraint is tight, and a scaling of the constraint gradient at those states.  ...  points via the KKT conditions, and then training a GPrepresented constraint that is consistent with and generalizes this data.  ... 
arXiv:2112.04612v2 fatcat:rwhmj2tv7ngy5iqhauemco5c6y

Uncertainty-Constrained Differential Dynamic Programming in Belief Space for Vision Based Robots [article]

Shatil Rahman, Steven L. Waslander
2020 arXiv   pre-print
We therefore propose a novel trajectory optimization formulation that incorporates inequality constraints on uncertainty and a novel Augmented Lagrangian based stochastic differential dynamic programming  ...  Our simulation tests demonstrate that our method can handle inequality constraints in different environments, for holonomic and nonholonomic motion models with no manual tuning of uncertainty costs involved  ...  amenable to gradient-based optimization.  ... 
arXiv:2012.00218v1 fatcat:rkshy7p4vvftpiup2rpwxpszmq

Weight Learning in a Probabilistic Extension of Answer Set Programs [article]

Joohyung Lee, Yi Wang
2018 arXiv   pre-print
LPMLN is a probabilistic extension of answer set programs with the weight scheme derived from that of Markov Logic. Previous work has shown how inference in LPMLN can be achieved.  ...  Learning in LPMLN is in accordance with the stable model semantics, thereby it learns parameters for probabilistic extensions of knowledge-rich domains where answer set programming has shown to be useful  ...  This work was partially supported by the National Science Foundation under Grants IIS-1526301 and IIS-1815337.  ... 
arXiv:1808.04527v2 fatcat:v2mobuf6ojamlfj4utxtjmablq

Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming

Chao Ning, Fengqi You
2019 Computers and Chemical Engineering  
Perspectives on online learning-based data-driven multistage optimization with a learning-while-optimizing scheme is presented.  ...  machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities.  ...  of deep learning in optimization under uncertainty are further suggested.  ... 
doi:10.1016/j.compchemeng.2019.03.034 fatcat:75rchq2egvdsfefuw6tvakodau

Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization

Kun-Yu Wang, Anthony Man-Cho So, Tsung-Hui Chang, Wing-Kin Ma, Chong-Yung Chi
2014 IEEE Transactions on Signal Processing  
In this paper, we study a probabilistically robust transmit optimization problem under imperfect channel state information (CSI) at the transmitter and under the multiuser multiple-input single-output  ...  probabilistic techniques.  ...  The third is the outage-based approach, whose design focus is on constraining QoS outages under a probabilistic CSI error model.  ... 
doi:10.1109/tsp.2014.2354312 fatcat:zu5wpb4gnjfjtptfy7llafjshy

Algorithmic Procedures for Stochastic Optimization [chapter]

Roger J. B. Wets
1985 Computational Mathematical Programming  
The algorithm given for stochastic programs with probabilistic constraints is due to E.  ...  Excluding certain specific classes of problems, such as stochastic programs with simple recourse and some stochastic programs with probabilistic constraints, where the properties of the problem at hand  ... 
doi:10.1007/978-3-642-82450-0_11 fatcat:jlxpdrcrfzg2vhzoguu6ydtt6q

An investigation of genetic algorithms for the optimization of multi-objective fisheries bioeconomic models

S.J. Mardle, S. Pascoe, M. Tamiz
2000 International Transactions in Operational Research  
/deterministic), tight variable bounds, weighting strategies and constraints.  ...  GENOCOP III has been designed to cater for non-linear constraints applying techniques to maintain feasibility of individual solutions.  ... 
doi:10.1111/j.1475-3995.2000.tb00183.x fatcat:ewhlhgojkvhp5epdh2zqukt7he

Distribution System Voltage Control under Uncertainties using Tractable Chance Constraints [article]

Pan Li, Baihong Jin, Dai Wang, Baosen Zhang
2018 arXiv   pre-print
We also show that this optimization problem is convex for a wide variety of probabilistic distributions.  ...  We show how the problem can be solved efficiently using historical samples via a stochastic quasi gradient method.  ...  With uncertainties introduced by the operation of the DERs, we model this constraint in a probabilistic fashion.  ... 
arXiv:1704.08999v4 fatcat:jsmebhmla5ewjiohk4sovrgtie

Probabilistic Inductive Querying Using ProbLog [chapter]

Luc De Raedt, Angelika Kimmig, Bernd Gutmann, Kristian Kersting, Vítor Santos Costa, Hannu Toivonen
2010 Inductive Databases and Constraint-Based Data Mining  
We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog.  ...  ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms.  ...  This work is partially supported by IQ (European Union Project IST-FET FP6-516169) and the GOA project 2008/08 Probabilistic Logic Learning.  ... 
doi:10.1007/978-1-4419-7738-0_10 fatcat:lpistivbwbgg5pjdq5o5orryl4

Heavy-traffic revenue maximization in parallel multiclass queues

Jonatha Anselmi, Giuliano Casale
2013 Performance evaluation (Print)  
In the general case with M queues and R classes, we prove that our heuristic is (1 + 1 M −1 )-competitive in heavy-traffic.  ...  Our main result is a simple heuristic that is able to provide tight guarantees on the optimality gap of its solutions.  ...  This is only due to constraint (18b), which is bilinear. Bilinear programs are known to be NP-hard, in general [11] .  ... 
doi:10.1016/j.peva.2013.08.008 fatcat:dwx7asgbibcw7hbtr4dry65c6y

Hybrid optimization of preparative chromatography for a ternary monoclonal antibody mixture

Vivien Fischer, Richard Kucia‐Tran, Will Lewis, Ajoy Velayudhan
2019 Biotechnology progress (Print)  
The hybrid optimization approach is shown to be extremely effective in dealing with this complex separation that was subject to multiple constraints based on yield, purity, and product breakthrough.  ...  Increased design space understanding was gained through the application of Monte Carlo simulations.  ...  An initial DoE outline was produced in discussion with Sophie Russell of GlaxoSmithKline; her input is greatly appreciated. This article is protected by copyright. All rights reserved.  ... 
doi:10.1002/btpr.2849 pmid:31121081 fatcat:mbf7rclasrhf5futz22nfgstku

Asymptotic Theory for Solutions in Statistical Estimation and Stochastic Programming

Alan J. King, R. Tyrrell Rockafellar
1993 Mathematics of Operations Research  
New techniques of local sensitivity analysis for nonsmooth generalized equations are applied to the study of sequences of statistical estimates and empirical approximations to solutions of stochastic programs  ...  in situations where estimates are subjected to constraints and estimation functionals are nonsmooth.  ...  This form is a mathematically convenient generalization of the usual statement of a nonlinear program with equality and inequality constraints (which can be obtained by setting Q = IR m 1 × IR m 2 + );  ... 
doi:10.1287/moor.18.1.148 fatcat:k3ejxnkkpbgnfhkhws5wwkkusa

Constraint-Based Local Search for the Automatic Generation of Architectural Tests [chapter]

Pascal Van Hentenryck, Carleton Coffrin, Boris Gutkovich
2009 Lecture Notes in Computer Science  
Moreover, the goal is to generate a large number of diverse solutions under tight runtime constraints.  ...  AT-GPs are complex conditional constraint satisfaction problems which typically feature both hard and soft constraints and very large domains (e.g., all memory addresses).  ...  For each probabilistic constraint, the search flips a coin and imposes the appropriate constraint based on the provided distribution.  ... 
doi:10.1007/978-3-642-04244-7_61 fatcat:6hly2rnhqnfaxb6r6smk24fqdi
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