Skip to main content
Critical incident 19/10/21
Berwick campus is closed under current Victorian Government lockdown restrictions. Click here for more information.

Professor Adil Baghirov

Professor, Applied Mathematics, Statistics and Optimisation

School of Engineering, IT and Phys. Sci.

Section/Portfolio:

Mathematics

Location:

Mt Helen Campus, Online

Aggregate subgradient method for nonsmooth DC optimization

The aggregate subgradient method is developed for solving unconstrained nonsmooth difference of...

Incremental DC optimization algorithm for large-scale clusterwise linear regression

The objective function in the nonsmooth optimization model of the clusterwise linear regression...

AN INCREMENTAL NONSMOOTH OPTIMIZATION ALGORITHM FOR CLUSTERING USING L-1 AND L-infinity NORMS

An algorithm is developed for solving clustering problems with the similarity measure defined...

Bundle methods for nonsmooth DC optimization

This chapter is devoted to algorithms for solving nonsmooth unconstrained difference of convex...

Clusterwise support vector linear regression

In clusterwise linear regression (CLR), the aim is to simultaneously partition data into a given...

Cyberattack triage using incremental clustering for intrusion detection systems

Intrusion detection systems (IDSs) are devices or software applications that monitor networks or...

Discrete gradient methods

In this chapter, the notion of a discrete gradient is introduced and it is shown that the...

Final words - Numerical Nonsmooth Optimization: State of the Art Algorithms

[No abstract available]

  • Book Chapters

Introduction - Numerical Nonsmooth Optimization: State of the Art Algorithms

Nonsmooth optimization (NSO) is among the most challenging tasks in the field of mathematical...

Missing Value Imputation via Clusterwise Linear Regression

In this paper a new method of preprocessing incomplete data is introduced. The method is based on...

New gene selection algorithm using hypeboxes to improve performance of classifiers

The use of DNA microarray technology allows to measure the expression levels of thousands of...

Numerical nonsmooth optimization: State of the art algorithms

Solving nonsmooth optimization (NSO) problems is critical in many practical applications and...

Partial Undersampling of Imbalanced Data for Cyber Threats Detection

Real-time detection of cyber threats is a challenging task in cyber security. With the...

Prediction of gold-bearing localised occurrences from limited exploration data

Inaccurate drill-core assay interpretation in the exploration stage presents challenges to...

Preface-Numerical Nonsmooth Optimization: State of the Art Algorithms

[No abstract available]

  • Book Chapters

Robust piecewise linear L <inf>1</inf>-regression via nonsmooth DC optimization

Piecewise linear (Formula presented.) -regression problem is formulated as an unconstrained...

The non-smooth and bi-objective team orienteering problem with soft constraints

In the classical team orienteering problem (TOP), a fixed fleet of vehicles is employed, each of...

A biased-randomised algorithm for the capacitated facility location problem with soft constraints

This paper analyzes the single-source capacitated facility location problem (SSCFLP) with soft...

A Comparative Study of Unsupervised Classification Algorithms in Multi-Sized Data Sets

The ability to mine and extract useful information automatically, from large data sets, is a...

A difference of convex optimization algorithm for piecewise linear regression

The problem of finding a continuous piecewise linear function approximating a regression function...

An approximate ADMM for solving linearly constrained nonsmooth optimization problems with two blocks of variables

Nonsmooth convex optimization problems with two blocks of variables subject to linear constraints...

A sharp augmented Lagrangian-based method in constrained non-convex optimization

In this paper, a novel sharp Augmented Lagrangian-based global optimization method is developed...

A simulated annealing-based maximum-margin clustering algorithm

Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It...

Multi-source cyber-attacks detection using machine learning

The Internet of Things (IoT) has significantly increased the number of devices connected to the...

A Comparative Assessment of Models to Predict Monthly Rainfall in Australia

Accurate rainfall prediction is a challenging task. It is especially challenging in Australia...

A server side solution for detecting webInject: A machine learning approach

With the advancement of client-side on the fly web content generation techniques, it becomes...

Clustering in large data sets with the limited memory bundle method

The aim of this paper is to design an algorithm based on nonsmooth optimization techniques to...

Double bundle method for finding clarke stationary points in nonsmooth dc programming?

The aim of this paper is to introduce a new proximal double bundle method for unconstrained...

Minimizing nonsmooth DC functions via successive DC piecewise-affine approximations

We introduce a proximal bundle method for the numerical minimization of a nonsmooth...

Nonsmooth DC programming approach to clusterwise linear regression: optimality conditions and algorithms

The clusterwise linear regression problem is formulated as a nonsmooth nonconvex optimization...

Preface of the special issue: the 6th International Conference on Optimization and Control with Applications (6th OCA)

Solving minimax problems: Local smoothing versus global smoothing

The aim of this chapter is to compare different smoothing techniques for solving finite minimax...

A proximal bundle method for nonsmooth DC optimization utilizing nonconvex cutting planes

In this paper, we develop a version of the bundle method to solve unconstrained difference of...

DC Programming Algorithm for Clusterwise Linear L1 Regression

The aim of this paper is to develop an algorithm for solving the clusterwise linear least...

New diagonal bundle method for clustering problems in large data sets

Clustering is one of the most important tasks in data mining. Recent developments in computer...

Optimization Based Clustering Algorithms for Authorship Analysis of Phishing Emails

Phishing has given attackers power to masquerade as legitimate users of organizations, such as...

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly...

AN ALGORITHM FOR CLUSTERING USING L-1-NORM BASED ON HYPERBOLIC SMOOTHING TECHNIQUE

Cluster analysis deals with the problem of organization of a collection of objects into clusters...

Batch clustering algorithm for big data sets

Vast spread of computing technologies has led to abundance of large data sets. Today tech...

Constrained Self Organizing Maps for Data Clusters Visualization

High dimensional data visualization is one of the main tasks in the field of data mining and...

Nonsmooth DC programming approach to the minimum sum-of-squares clustering problems

This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems...

A history of water distribution systems and their optimisation

Water distribution systems have a very long and rich history dating back to the third millennium...

An algorithm for clusterwise linear regression based on smoothing techniques

We propose an algorithm based on an incremental approach and smoothing techniques to solve...

An incremental clustering algorithm based on hyperbolic smoothing

Clustering is an important problem in data mining. It can be formulated as a nonsmooth, nonconvex...

An incremental piecewise linear classifier based on polyhedral conic separation

In this paper, a piecewise linear classifier based on polyhedral conic separation is developed....

Diagnostic with incomplete nominal/discrete data

Exploration of the Trade-Offs between Water Quality and Pumping Costs in Optimal Operation of Regional Multiquality Water Distribution Systems

This paper explores the trade-offs between water quality and pumping costs objectives in...

Impact of Water-Quality Conditions in Source Reservoirs on the Optimal Operation of a Regional Multiquality Water-Distribution System

The impact of water quality conditions in source reservoirs on the optimal operation of a...

Modified self-organising maps with a new topology and initialisation algorithm

Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and...

Nonsmooth Optimization Algorithm for Solving Clusterwise Linear Regression Problems

Clusterwise linear regression consists of finding a number of linear regression functions each...

Nonsmooth optimization based algorithms in cluster analysis

Sensitivity of algorithm parameters and objective function scaling in multi-objective optimisation of water distribution systems

This paper presents an extensive analysis of the sensitivity of multi-objective algorithm...

Solving DC programs using the cutting angle method

In this paper, we propose a new algorithm for global minimization of functions represented as a...

A convolutional recursive modified Self Organizing Map for handwritten digits recognition

It is well known that the handwritten digits recognition is a challenging problem. Different...

Aggregate codifferential method for nonsmooth DC optimization

A new algorithm is developed based on the concept of codifferential for minimizing the difference...

A heuristic algorithm for solving the minimum sum-of-squares clustering problems

Clustering is an important task in data mining. It can be formulated as a global optimization...

CR-Modified SOM to the Problem of Handwritten Digits Recognition

Recently, researchers show that the handwritten digit recognition is a challenging problem. In...

Introduction to Nonsmooth Optimization: Theory, Practice and Software

This book is the first easy-to-read text on nonsmooth optimization (NSO, not necessarily...

Optimal operation of a multi-quality water distribution system with changing turbidity and salinity levels in source reservoirs

Piecewise linear classifiers based on nonsmooth optimization approaches

Preface of the special issue OR: Connecting sciences supported by global optimization related to the 25th European conference on operational research (EURO XXV 2012)

[No abstract available]

An algorithm for minimization of pumping costs in water distribution systems using a novel approach to pump scheduling

The operation of a water distribution system is a complex task which involves scheduling of...

A new modification of Kohonen neural network for VQ and clustering problems

  • Conference Proceedings

A novel piecewise linear classifier based on polyhedral conic and max-min separabilities

In this paper, an algorithm for finding piecewise linear boundaries between pattern classes is...

Capped K-NN Editing in Definition Lacking Environments

Hyperbolic smoothing function method for minimax problems

In this article, an approach for solving finite minimax problems is proposed. This approach is...

Nonsmooth nonconvex optimization approach to clusterwise linear regression problems

Clusterwise regression consists of finding a number of regression functions each approximating a...

Pumping Costs and Water Quality in the Battlefield of Optimal Operation of Water Distribution Networks

  • Conference Proceedings

Subgradient and bundle methods for nonsmooth optimization

Subgradient Method for Nonconvex Nonsmooth Optimization

In this paper, we introduce a new method for solving nonconvex nonsmooth optimization problems....

A novel approach to optimal pump scheduling in water distribution systems

  • Conference Proceedings

Application of Optimisation-based Data Mining Techniques to Medical Data Sets: A Comparative Analysis

  • Conference Proceedings

Comparing different nonsmooth minimization methods and software

Most nonsmooth optimization (NSO) methods can be divided into two main groups: subgradient...

Comparison of metaheuristic algorithms for pump operation optimization

  • Conference Proceedings

Framework for Multi-objective Optimisation of the Operation of Water Distribution Networks including Water Quality

  • Conference Proceedings

Limited memory discrete gradient bundle method for nonsmooth derivative-free optimization

Typically, practical nonsmooth optimization problems involve functions with hundreds of...

Machine learning algorithms for analysis of DNA data sets

Minimization of pumping costs in water distribution systems using explicit and implicit pump scheduling

  • Conference Proceedings

An efficient algorithm for the incremental construction of a piecewise linear classifier

In this paper the problem of finding piecewise linear boundaries between sets is considered and...

A novel hybrid neural learning algorithm using simulated annealing and quasisecant method

In this paper, we propose a hybrid learning algorithm for the single hidden layer feedforward...

  • Conference Proceedings

Classification through incremental max-min separability

Piecewise linear functions can be used to approximate non-linear decision boundaries between...

Codifferential method for minimizing nonsmooth DC functions

Fast modified global k-means algorithm for incremental cluster construction

The k-means algorithm and its variations are known to be fast clustering algorithms. However,...

Feature selection using misclassification counts

  • Conference Proceedings

Improving Naive Bayes classifier using conditional probabilities

  • Conference Proceedings

Adaptation to water shortage through the implementation of a unique pipeline system in Victoria, Australia

  • Conference Proceedings

A generalized subgradient method with piecewise linear subproblem

In this paper, a new version of the quasisecant method for nonsmooth nonconvex optimization is...

  • Journals

Alexander rubinov - An outstanding scholar

  • Journals

An algorithm for the estimation of a regression function by continuous piecewise linear functions

The problem of the estimation of a regression function by continuous piecewise linear functions...

An L2-boosting algorithm for estimation of a regression function

An L2-boosting algorithm for estimation of a regression function from random design is presented,...

Application of optimisation-based data mining techniques to tobacco control dataset

  • Journals

A quasisecant method for minimizing nonsmooth functions

We present an algorithm to locally minimize nonsmooth, nonconvex functions. In order to find...

Cluster analysis of a tobacco control data set

  • Journals

Truncated codifferential method for linearly constrained nonsmooth optimization

In this paper a new algorithm is developed to minimize linearly constrained non-smooth...

  • Conference Proceedings

Truncated codifferential method for nonsmooth convex optimization

In this paper a new algorithm to minimize convex functions is developed. This algorithm is based...

  • Journals

A multidimensional descent method for global optimization

A new modified global k-means algorithm for clustering large data sets

  • Conference Proceedings

An incremental approach for the classification of a piecewise linear classifier

  • Conference Proceedings

Comments on: Optimization and data mining in medicine

Continuous Approximations to Subdifferentials

  • Book Chapters

Derivative-Free Methods for Non-Smooth Optimization

  • Book Chapters

Estimation of a Regression Function by Maxima of Minima of Linear Functions

In this paper, estimation of a regression function from independent and identically distributed...

Global Optimization: Cutting Angle Method

  • Book Chapters

Nonsmooth Optimization Approach to Clustering

  • Book Chapters

Optimization methods and the K-committees algorithm for clustering of sequence data

  • Journals

An approximate subgradient algorithm for unconstrained nonsmooth, nonconvex optimization

In this paper a new algorithm for minimizing locally Lipschitz functions is developed. Descent...

Discrete Gradient Method: Derivative-Free Method for Nonsmooth Optimization

A new derivative-free method is developed for solving unconstrained nonsmooth optimization...

Modified global k-means algorithm for minimum sum-of-squares clustering problems

Optimisation of operations of a water distribution system for reduced power usage

  • Conference Proceedings

A nonsmooth optimization approach to sensor network localization

In this paper the problem of localization of wireless sensor network is formulated as an...

Integrated production system optimization using global optimization techniques

Many optimization problems related to integrated oil and gas production systems are nonconvex and...

  • Journals

Preface

[No abstract available]

Visual Tools for Analysing Evolution, Emergence and Error in Data Streams

The relatively new field of stream mining has necessitated the development of robust drift-aware...

A derivative-free method for linearly constrained nonsmooth optimization

  • Journals

A hybrid neural learning algorithm using evolutionary learning and derivative free local search method

A new nonsmooth optimization algorithm for minimum sum-of-squares clustering problems

Application of derivative free methods for production optimization

  • Journals

Modified global k-means algorithm for clustering in gene expression data sets

  • Conference Proceedings

Non-smooth optimization methods for computation of the conditional value-at-risk and portfolio optimization

We examine numerical performance of various methods of calculation of the Conditional...

Piecewise Partially Separable Functions and a Derivative-free Algorithm for Large Scale Nonsmooth Optimization

An algorithm for minimizing clustering functions

Comparative analysis of genetic algorithm, simulated annealing and cutting angle method for artificial neural networks

  • Book Chapters

Comparative analysis of genetic algorithm vs. evolutionary algorithm for hybrid models with discrete gradient method for artificial neural network

  • Conference Proceedings

Data mining with combined use of optimization techniques and self-organizing maps for improving risk grouping rules: application to prostate cancer patients

Data mining techniques provide a popular and powerful tool set to generate various data-driven...

Determining regularization parameters for derivative free neural learning

Derivative free optimisation methods have recently gained a loi of attractions for neural...

  • Conference Proceedings

Fusion strategies for neural learning algorithms using evolutionary and discrete gradient approaches

  • Conference Proceedings

Hybridization of neural learning algorithms using evolutionary and discrete gradient approaches

  • Journals

Local optimization method with global multidimensional search

Max-min separability

On minimization of max-min functions

  • Book Chapters

Supervised data classification via max-min separability

  • Book Chapters

A hybrid neural learning algorithm combining evolutionary algorithm with discrete gradient method

  • Conference Proceedings

Improving risk grouping rules for prostate cancer patients with optimization

Data mining techniques provide a popular and powerful toolset to address both clinical and...

  • Conference Proceedings

Optimization of feed forward MLPs using the discrete gradient method

  • Conference Proceedings

Separation of two sets by piecewise linear function

  • Journals

Solving Euclidian travelling salesman problem using discrete-gradient based clustering and kohonen neural network

  • Conference Proceedings

An algorithm for clustering based on non-smooth optimization techniques

The problem of cluster analysis is formulated as a problem of non-smooth, non-convex...

An optimization-based approach to patient grouping for acute healthcare in Australia

The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization,...

  • Conference Proceedings

Comparative analysis of the cutting angle and simulated annealing methods in global optimization

Continuous subdifferential approximations and their applications

  • Journals

Cutting angle method and a local search

The paper deals with combinations of the cutting angle method in global optimization and a local...

Hybrid simulated annealing and discrete gradient method for global optimization

  • Conference Proceedings

Lagrange-type functions in constrained optimization

  • Journals

New algorithms for multi-class cancer diagnosis using tumor gene expression signatures

Optimization based clustering algorithms in multicast group hierarchies

  • Conference Proceedings

Parallelization of the discrete gradient method of non-smooth optimization and its applications

We investigate parallelization and performance of the discrete gradient method of nonsmooth...

  • Conference Proceedings

Penalty functions with a small penalty parameter: Numerical experiments

  • Conference Proceedings

The discrete gradient evolutionary strategy method for global optimization

Unsupervised and supervised data classification via nonsmooth and global optimization

  • Journals

A global optimization approach to classification

  • Journals

A heuristic algorithm for feature selection based on optimization techniques

The feature selection problem involves the selection of a subset of features that will be...

A method for minimization of quasidifferentiable functions

A method of truncated codifferential with application to some problems of cluster analysis

  • Journals

Penalty functions with a small penalty parameter

A global optimisation approach to classification in medical diagnosis and prognosis

A global optimization approach to classification in medical diagnosis and prognosis

  • Conference Proceedings

Clustering via D.C. optimization

  • Book Chapters

Discrete gradient method in nonsmooth optimization

  • Conference Proceedings

Global optimization of marginal functions with applications to economic equilibrium