Jump to ContentJump to Main Navigation

You are looking at 1-5 of 5 items

  • Keywords: convex optimization x
Clear All Modify Search

View:

Definitions—Background

Pierre-Loïc Garoche

in Formal Verification of Control System Software

Published in print:
2019
Published Online:
January 2020
ISBN:
9780691181301
eISBN:
9780691189581
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691181301.003.0004
Subject:
Mathematics, Applied Mathematics

This chapter presents the formalisms describing discrete dynamical systems and gives an overview on the convex optimization tools and methods used to compute the analyses. A dynamical system is a ... More


Convex Optimization and Numerical Issues

Pierre-Loïc Garoche

in Formal Verification of Control System Software

Published in print:
2019
Published Online:
January 2020
ISBN:
9780691181301
eISBN:
9780691189581
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691181301.003.0010
Subject:
Mathematics, Applied Mathematics

This chapter aims at providing the intuition behind convex optimization algorithms and addresses their effective use with floating-point implementation. It first briefly presents the algorithms, ... More


Invariant Synthesis via Convex Optimization: Postfixpoint Computation as Semialgebraic Constraints

Pierre-Loïc Garoche

in Formal Verification of Control System Software

Published in print:
2019
Published Online:
January 2020
ISBN:
9780691181301
eISBN:
9780691189581
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691181301.003.0005
Subject:
Mathematics, Applied Mathematics

This chapter focuses on the computation of invariant for a discrete dynamical system collecting semantics. Invariants or collecting semantics properties are properties preserved along all executions ... More


Graph Kernels by Spectral Transforms

Zhu Xiaojin, Kandola Jaz, Lafferty John, and Ghahramani Zoubin

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0015
Subject:
Computer Science, Machine Learning

This chapter develops an approach to searching over a nonparametric family of spectral transforms by using convex optimization to maximize kernel alignment to the labeled data. Order constraints are ... More


Prediction of Protein Function from Networks

Shin Hyunjung and Tsuda Koji

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0020
Subject:
Computer Science, Machine Learning

This chapter describes an algorithm to assign weights to multiple graphs within graph-based semi-supervised learning. Both predicting class labels and searching for weights for combining multiple ... More


View: