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Learning-Based Abstractions for Nonlinear Constraint Solving
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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We propose a new abstraction refinement procedure based on machine learning to improve the performance of nonlinear constraint solving algorithms on large-scale problems. ...
The mechanism is capable of producing intermediate symbolic abstractions that are also important for many applications and for understanding the internal structures of hard constraint solving problems. ...
Burdick for helpful input. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ...
doi:10.24963/ijcai.2017/83
dblp:conf/ijcai/DathathriAGM17
fatcat:cv3fh5nmyzfphnevgfwv5k46ju
Learning Neural Networks under Input-Output Specifications
[article]
2022
arXiv
pre-print
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To this end, we address the key technical challenge of convexifying the verification condition for neural networks, which is derived by abstracting the nonlinear specifications and activation functions ...
with quadratic constraints. ...
SPECIFICATION ANALYSIS FOR A FIXED NN We now briefly review the analysis conducted in [17] based on the framework of quadratic constraints. ...
arXiv:2202.11246v1
fatcat:pmfl3xmlarhc3hg47ksc67rpoa
A Distributed Primal Decomposition Scheme for Nonconvex Optimization
2019
IFAC-PapersOnLine
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Abstract: In this paper, we deal with large-scale nonconvex optimization problems, typically arising in distributed nonlinear optimal control, that must be solved by agents in a network. ...
Abstract: In this paper, we deal with large-scale nonconvex optimization problems, typically arising in distributed nonlinear optimal control, that must be solved by agents in a network. ...
Our distributed algorithm enjoys Abstract: In this paper, we deal with large-scale nonconvex optimization problems, typically arising in distributed nonlinear optimal control, that must be solved by agents ...
doi:10.1016/j.ifacol.2019.12.174
fatcat:irhzgdtrxbbqlbmz6b3bgxhcze
A Review of Constraint Programming
2014
International Journal of Computer Applications Technology and Research
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Constraint programming is based on feasibility which means finding a feasible solution rather than optimization which means finding an optimal solution and focuses on the constraints and variables domain ...
A model could be very hard to solve if it is poorly chosen. ...
Numerical result for solving large scale nonlinear optimization problems is presented. The performance of each solver is explained easily and predicted based on the characteristics. ...
doi:10.7753/ijcatr0307.1001
fatcat:fhokqi424bb57ieuujgunfq5k4
Speeding up problem solving by abstraction: a graph oriented approach
1996
Artificial Intelligence
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Pearl, Uncovering trees in constraint networks B.J. Grosz and S. Kraus, Collaborative plans for complex group action ...
Graph search methods for non-order-preserving evaluation functions: applications to job sequencing problems J. Lin, A semantics for reasoning consistently in the presence of inconsistency R. ...
MacDonald, Speeding up problem solving by abstraction: a graph oriented approach Thl\ paper presents a new perspcctlve on the traditional Al task of problem solving and the techniques of abstraction and ...
doi:10.1016/0004-3702(96)81372-5
fatcat:bhdyzgaxhrhepjckt2iqfjp2xm
Exploratory analysis of speedup learning data using expectation maximization
1996
Artificial Intelligence
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Pearl, Uncovering trees in constraint networks B.J. Grosz and S. Kraus, Collaborative plans for complex group action ...
Graph search methods for non-order-preserving evaluation functions: applications to job sequencing problems J. Lin, A semantics for reasoning consistently in the presence of inconsistency R. ...
MacDonald, Speeding up problem solving by abstraction: a graph oriented approach Thl\ paper presents a new perspcctlve on the traditional Al task of problem solving and the techniques of abstraction and ...
doi:10.1016/0004-3702(96)81373-7
fatcat:vznawqt52beqpeedmeens7t7dq
Modular strategic SMT solving with SMT-RAT
2018
Acta Universitatis Sapientiae: Informatica
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As a distinguishing feature, SMT-RAT provides a set of solving modules and supports their strategic combination. ...
In this paper we present the latest developments in SMT-RAT, a tool for the automated check of quantifier-free real and integer arithmetic formulas for satisfiability. ...
Learning the Boolean abstraction of this explanation refines the Boolean abstraction, avoiding Boolean solutions with the same theory conflict in future search. ...
doi:10.2478/ausi-2018-0001
fatcat:eedfeixfz5fc5jdph2g6rzrcye
Planning and Operations Research (Dagstuhl Seminar 18071)
2018
Dagstuhl Reports
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The seminar brought together researchers in the areas of Artificial Intelligence (AI) Planning, Constraint Programming, and Operations Research. ...
of artificial intelligence where the emphasis was traditionally more on symbolic and logical search techniques for the intelligent selection and sequencing of actions to achieve a set of goals. ...
We presented a constraint-based declarative model for MAPF, together with its implementation in Picat, a logic-based programming language. ...
doi:10.4230/dagrep.8.2.26
dblp:journals/dagstuhl-reports/BeckMRH18
fatcat:lavt5jfujfarfmtwrpbbxan2oq
Exploring case-Based building design—CADRE
1993
Artificial intelligence for engineering design, analysis and manufacturing
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We describe the problems and the ways we either solved or worked around them in the CADRE system. ...
In our work on CADRE, a case-based building design system, we have encountered seven fundamental problems which we think are common to most case-based design systems. ...
Conclusion: How far can the potential advantages of case-based design be achieved? Case-based reasoning has been credited for its advantages in solving design problems. ...
doi:10.1017/s0890060400000822
fatcat:e7ytoz67mva7lorol7okl37eye
Optimal Path Analysis for Solving Nonlinear Equations with Finite Local Error
2022
North atlantic university union: International Journal of Circuits, Systems and Signal Processing
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Because the traditional method of solving nonlinear equations takes a long time, an optimal path analysis method for solving nonlinear equations with limited local error is designed. ...
Secondly, set the constraints of the objective function, solve the optimal solution of the nonlinear equation under the condition of limited local error, and obtain the optimal path of the nonlinear equation ...
The vast majority of methods for solving nonlinear equations are iterative methods. ...
doi:10.46300/9106.2022.16.13
fatcat:uufdqmtfcnefnmygty5i734g6u
Maximum Likelihood Methods for Inverse Learning of Optimal Controllers
2020
IFAC-PapersOnLine
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Abstract: This paper presents a framework for inverse learning of objective functions for constrained optimal control problems, which is based on the Karush-Kuhn-Tucker (KKT) conditions. ...
Abstract: This paper presents a framework for inverse learning of objective functions for constrained optimal control problems, which is based on the Karush-Kuhn-Tucker (KKT) conditions. ...
In simulation, we present learning results for both constrained, linear and nonlinear systems. ...
doi:10.1016/j.ifacol.2020.12.1206
fatcat:jyzclz3przdkzozoqesxw5jfma
Page 4238 of Mathematical Reviews Vol. , Issue 2004e
[page]
2004
Mathematical Reviews
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The code can also solve second-order conic pro- gramming (SOCP) problems, as well as problems with a mixture of SDP, SOCP and NLP constraints. ...
Unsupervised learning (i.e., clustering) is done
by unconstrained minimization of a non-smooth cluster function,
and supervised learning (i.e., classification) then is performed by
solving the cluster ...
Data-Driven Control of Unknown Systems: A Linear Programming Approach
2020
IFAC-PapersOnLine
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Abstract: We consider the problem of discounted optimal state-feedback regulation for general unknown deterministic discrete-time systems. ...
Abstract: We consider the problem of discounted optimal state-feedback regulation for general unknown deterministic discrete-time systems. ...
, utilizing a heuristic based on support constraints. ...
doi:10.1016/j.ifacol.2020.12.027
fatcat:3psowni2erfflif7h5rq3e64iy
Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model
2013
IEEE Transactions on Signal Processing
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Index Terms-Hyperspectral imaging, multi-kernel learning, nonlinear spectral unmixing, support vector regression. ...
In this paper, we formulate a new kernel-based paradigm that relies on the assumption that the mixing mechanism can be described by a linear mixture of endmember spectra, with additive nonlinear fluctuations ...
The abundances are determined by solving an appropriate kernel-based regression problem under constraints. This paper is organized as follows. ...
doi:10.1109/tsp.2012.2222390
fatcat:m35fa2xisrcm5o42zhwm4bk7j4
Page 1178 of Mathematical Reviews Vol. , Issue 97B
[page]
1997
Mathematical Reviews
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For nonlinear problems, it is shown that the concepts of complexity based on adaptive information are helpful. ...
binary search, min- imax optimization of unimodal functions and solving of some nonlinear equations. ...
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