Taking inspiration from approximate ranking, this paper investigates the use of rank-based Support Vector Machine as surrogate model within CMA-ES, enforcing the invariance of the approach with respect to monotonous transformations of the fitness function. Whereas the choice of the SVM kernel is known to be a critical issue, the proposed approach uses the Covariance Matrix adapted by CMA-ES within a Gaussian kernel, ensuring the adaptation of the kernel to the currently explored region of the fitness landscape at almost no computational overhead. The empirical validation of the approach on standard benchmarks, comparatively to CMA-ES and recent surrogate-based CMA-ES, demonstrates the efficiency and scalability of the proposed approach.
1. The document discusses learned visual representations for object detection from images.
2. It describes how earlier models were hand-coded while modern approaches learn the model structure and filters from labeled training data.
3. The learned models are defined by a root filter and component filters that specify the location of each part relative to the root to account for spatial relationships.
D-Branes and The Disformal Dark Sector - Danielle Wills and Tomi KoivistoCosmoAIMS Bassett
The document discusses disformal relations between the physical and gravitational geometry. It begins by introducing the most general form such a relation could take, with two arbitrary functions C and D of a scalar field and its derivative.
It then discusses how this type of relation naturally arises in many modified gravity and scalar-tensor theories. Specific examples mentioned include f(R) gravity and the Dirac-Born-Infeld (DBI) string scenario.
The document outlines how a disformal coupling could have interesting phenomenological implications and be detectable through effects on cosmology and structure formation. It concludes by stating the disformal relation is an important generalization worth further study.
Plane rectification through robust vanishing point tracking using the expecta...Sergio Mancera
This document summarizes a paper that introduces a new strategy for plane rectification in image sequences based on the Expectation-Maximization (EM) algorithm. The approach estimates the dominant vanishing point and significant lines passing through it simultaneously. It defines a likelihood distribution for gradient image pixels considering position and orientation. The mixture model used by the EM algorithm includes an additional component to handle outliers. Synthetic data tests show the method's robustness and efficiency. Plane rectification results demonstrate removing perspective and affine distortion from real traffic sequences using one vanishing point.
The document proposes Adaptive Coordinate Descent (ACiD), which combines adaptive encoding inspired by principal component analysis with coordinate descent, to enable optimization of non-separable problems. ACiD adapts the coordinate system using an encoding matrix updated similarly to CMA-ES, and performs coordinate descent optimization in this adapted space. Experimental results show ACiD can optimize benchmark functions like the Rosenbrock function as fast as state-of-the-art evolutionary algorithms.
Intensive Surrogate Model Exploitation in Self-adaptive Surrogate-assisted CM...Ilya Loshchilov
1. The document presents an approach called saACM-ES for intensive exploitation of surrogate models in black-box optimization. saACM-ES uses a self-adaptive surrogate model to assist the CMA-ES algorithm.
2. A key idea is intensive surrogate model exploitation, where larger population sizes (e.g. 1000x default) are used when optimizing the surrogate model to allow for more intensive local search. This can provide faster convergence but risks divergence if the surrogate is imprecise.
3. Experimental results on black-box optimization benchmark problems show that the proposed approach, called saACM-k, often finds better solutions than the original saACM and other state-of-
Dominance-Based Pareto-Surrogate for Multi-Objective OptimizationIlya Loshchilov
This document proposes a dominance-based Pareto surrogate model for multi-objective optimization using support vector machines. The model learns primary and secondary dominance constraints to build a surrogate function that preserves the Pareto dominance relations of training points. Experimental results show that using the surrogate to guide multi-objective evolutionary algorithms leads to 1.5-5x speedups in converging to the Pareto front on test problems compared to the original algorithms. However, the surrogate may prematurely converge the diversity of solutions, as it only considers convergence and not diversity maintenance. The model can incorporate additional preferences beyond dominance to further improve optimization.
A Pareto-Compliant Surrogate Approach for Multiobjective OptimizationIlya Loshchilov
Most surrogate approaches to multi-objective optimization
build a surrogate model for each objective. These surrogates
can be used inside a classical Evolutionary Multiobjective
Optimization Algorithm (EMOA) in lieu of the actual objectives, without modifying the underlying EMOA; or to filter
out points that the models predict to be uninteresting. In
contrast, the proposed approach aims at building a global
surrogate model defined on the decision space and tightly
characterizing the current Pareto set and the dominated region, in order to speed up the evolution progress toward
the true Pareto set. This surrogate model is specified by
combining a One-class Support Vector Machine (SVMs) to
characterize the dominated points, and a Regression SVM to
clamp the Pareto front on a single value. The resulting surrogate model is then used within state-of-the-art EMOAs to
pre-screen the individuals generated by application of standard variation operators. Empirical validation on classical
MOO benchmark problems shows a significant reduction of
the number of evaluations of the actual objective functions.
1. The document discusses learned visual representations for object detection from images.
2. It describes how earlier models were hand-coded while modern approaches learn the model structure and filters from labeled training data.
3. The learned models are defined by a root filter and component filters that specify the location of each part relative to the root to account for spatial relationships.
D-Branes and The Disformal Dark Sector - Danielle Wills and Tomi KoivistoCosmoAIMS Bassett
The document discusses disformal relations between the physical and gravitational geometry. It begins by introducing the most general form such a relation could take, with two arbitrary functions C and D of a scalar field and its derivative.
It then discusses how this type of relation naturally arises in many modified gravity and scalar-tensor theories. Specific examples mentioned include f(R) gravity and the Dirac-Born-Infeld (DBI) string scenario.
The document outlines how a disformal coupling could have interesting phenomenological implications and be detectable through effects on cosmology and structure formation. It concludes by stating the disformal relation is an important generalization worth further study.
Plane rectification through robust vanishing point tracking using the expecta...Sergio Mancera
This document summarizes a paper that introduces a new strategy for plane rectification in image sequences based on the Expectation-Maximization (EM) algorithm. The approach estimates the dominant vanishing point and significant lines passing through it simultaneously. It defines a likelihood distribution for gradient image pixels considering position and orientation. The mixture model used by the EM algorithm includes an additional component to handle outliers. Synthetic data tests show the method's robustness and efficiency. Plane rectification results demonstrate removing perspective and affine distortion from real traffic sequences using one vanishing point.
The document proposes Adaptive Coordinate Descent (ACiD), which combines adaptive encoding inspired by principal component analysis with coordinate descent, to enable optimization of non-separable problems. ACiD adapts the coordinate system using an encoding matrix updated similarly to CMA-ES, and performs coordinate descent optimization in this adapted space. Experimental results show ACiD can optimize benchmark functions like the Rosenbrock function as fast as state-of-the-art evolutionary algorithms.
Intensive Surrogate Model Exploitation in Self-adaptive Surrogate-assisted CM...Ilya Loshchilov
1. The document presents an approach called saACM-ES for intensive exploitation of surrogate models in black-box optimization. saACM-ES uses a self-adaptive surrogate model to assist the CMA-ES algorithm.
2. A key idea is intensive surrogate model exploitation, where larger population sizes (e.g. 1000x default) are used when optimizing the surrogate model to allow for more intensive local search. This can provide faster convergence but risks divergence if the surrogate is imprecise.
3. Experimental results on black-box optimization benchmark problems show that the proposed approach, called saACM-k, often finds better solutions than the original saACM and other state-of-
Dominance-Based Pareto-Surrogate for Multi-Objective OptimizationIlya Loshchilov
This document proposes a dominance-based Pareto surrogate model for multi-objective optimization using support vector machines. The model learns primary and secondary dominance constraints to build a surrogate function that preserves the Pareto dominance relations of training points. Experimental results show that using the surrogate to guide multi-objective evolutionary algorithms leads to 1.5-5x speedups in converging to the Pareto front on test problems compared to the original algorithms. However, the surrogate may prematurely converge the diversity of solutions, as it only considers convergence and not diversity maintenance. The model can incorporate additional preferences beyond dominance to further improve optimization.
A Pareto-Compliant Surrogate Approach for Multiobjective OptimizationIlya Loshchilov
Most surrogate approaches to multi-objective optimization
build a surrogate model for each objective. These surrogates
can be used inside a classical Evolutionary Multiobjective
Optimization Algorithm (EMOA) in lieu of the actual objectives, without modifying the underlying EMOA; or to filter
out points that the models predict to be uninteresting. In
contrast, the proposed approach aims at building a global
surrogate model defined on the decision space and tightly
characterizing the current Pareto set and the dominated region, in order to speed up the evolution progress toward
the true Pareto set. This surrogate model is specified by
combining a One-class Support Vector Machine (SVMs) to
characterize the dominated points, and a Regression SVM to
clamp the Pareto front on a single value. The resulting surrogate model is then used within state-of-the-art EMOAs to
pre-screen the individuals generated by application of standard variation operators. Empirical validation on classical
MOO benchmark problems shows a significant reduction of
the number of evaluations of the actual objective functions.
Keynote given at the Asia Pacific Software Engineering Conference (APSEC), December 2020, on Automated Program Repair technologies and their applications.
System Verilog 2009 & 2012 enhancementsSubash John
This document summarizes enhancements made to System Verilog in 2009 and 2012. The 2009 enhancements included final blocks, bit selects of expressions, edge detection for DDR logic, fork-join improvements, and display enhancements. The 2012 enhancements extended enums, added scale factors to real constants and mixed-signal assertions, introduced aspect-oriented programming features, and removed X-optimism using new keywords. It also proposed signed operators and discussed some high-level problems not yet addressed.
Ontology mapping requires context, background knowledge, and approximation. Using background knowledge from multiple large ontologies can improve ontology mapping results between two target ontologies by discovering more matches. Exploiting the hierarchical structure in background ontologies through indirect subsumption reasoning can significantly increase the number of matches found. Allowing for approximate matches by introducing a "sloppiness" threshold based on the semantic distance between concepts can further improve results by discovering desirable matches while avoiding undesirable ones except at high sloppiness levels.
The document discusses the double modal transformation technique for analyzing the dynamic response of linear structures subjected to stochastic loading. It proposes a method called double modal transformation that simultaneously transforms the equations of motion and the loading process. This allows the structural response to be obtained through a double series expansion where structural and loading modal contributions are superimposed. The effectiveness of this technique is illustrated through two classic wind engineering problems: alongwind response and vortex-induced crosswind response of slender structures.
MATLAB sessions: Laboratory 2
MAT 275 Laboratory 2
Matrix Computations and Programming in MATLAB
In this laboratory session we will learn how to
1. Create and manipulate matrices and vectors.
2. Write simple programs in MATLAB
NOTE: For your lab write-up, follow the instructions of LAB1.
Matrices and Linear Algebra
⋆ Matrices can be constructed in MATLAB in different ways. For example the 3 × 3 matrix
A =
8 1 63 5 7
4 9 2
can be entered as
>> A=[8,1,6;3,5,7;4,9,2]
A =
8 1 6
3 5 7
4 9 2
or
>> A=[8,1,6;
3,5,7;
4,9,2]
A =
8 1 6
3 5 7
4 9 2
or defined as the concatenation of 3 rows
>> row1=[8,1,6]; row2=[3,5,7]; row3=[4,9,2]; A=[row1;row2;row3]
A =
8 1 6
3 5 7
4 9 2
or 3 columns
>> col1=[8;3;4]; col2=[1;5;9]; col3=[6;7;2]; A=[col1,col2,col3]
A =
8 1 6
3 5 7
4 9 2
Note the use of , and ;. Concatenated rows/columns must have the same length. Larger matrices can
be created from smaller ones in the same way:
c⃝2011 Stefania Tracogna, SoMSS, ASU
MATLAB sessions: Laboratory 2
>> C=[A,A] % Same as C=[A A]
C =
8 1 6 8 1 6
3 5 7 3 5 7
4 9 2 4 9 2
The matrix C has dimension 3 × 6 (“3 by 6”). On the other hand smaller matrices (submatrices) can
be extracted from any given matrix:
>> A(2,3) % coefficient of A in 2nd row, 3rd column
ans =
7
>> A(1,:) % 1st row of A
ans =
8 1 6
>> A(:,3) % 3rd column of A
ans =
6
7
2
>> A([1,3],[2,3]) % keep coefficients in rows 1 & 3 and columns 2 & 3
ans =
1 6
9 2
⋆ Some matrices are already predefined in MATLAB:
>> I=eye(3) % the Identity matrix
I =
1 0 0
0 1 0
0 0 1
>> magic(3)
ans =
8 1 6
3 5 7
4 9 2
(what is magic about this matrix?)
⋆ Matrices can be manipulated very easily in MATLAB (unlike Maple). Here are sample commands
to exercise with:
>> A=magic(3);
>> B=A’ % transpose of A, i.e, rows of B are columns of A
B =
8 3 4
1 5 9
6 7 2
>> A+B % sum of A and B
ans =
16 4 10
4 10 16
10 16 4
>> A*B % standard linear algebra matrix multiplication
ans =
101 71 53
c⃝2011 Stefania Tracogna, SoMSS, ASU
MATLAB sessions: Laboratory 2
71 83 71
53 71 101
>> A.*B % coefficient-wise multiplication
ans =
64 3 24
3 25 63
24 63 4
⋆ One MATLAB command is especially relevant when studying the solution of linear systems of dif-
ferentials equations: x=A\b determines the solution x = A−1b of the linear system Ax = b. Here is an
example:
>> A=magic(3);
>> z=[1,2,3]’ % same as z=[1;2;3]
z =
1
2
3
>> b=A*z
b =
28
34
28
>> x = A\b % solve the system Ax = b. Compare with the exact solution, z, defined above.
x =
1
2
3
>> y =inv(A)*b % solve the system using the inverse: less efficient and accurate
ans =
1.0000
2.0000
3.0000
Now let’s check for accuracy by evaluating the difference z − x and z − y. In exact arithmetic they
should both be zero since x, y and z all represent the solution to the system.
>> z - x % error for backslash command
ans =
0
0
0
>> z - y % error for inverse
ans =
1.0e-015 *
-0.4441
0
-0.88 ...
This document provides an overview of an introductory course on using R for statistical analysis. It covers topics such as the R environment and language, working with objects and data types, importing and manipulating data, and performing basic analyses and visualizations. The course materials are divided into sections covering the R workspace, reading and writing data, data manipulation, plotting, and more advanced techniques. Examples are provided throughout to demonstrate key R functions and capabilities.
1) The document describes a MATLAB orientation course organized by FOCUS-R&D.
2) The course covers fundamentals of MATLAB including programming basics, plotting, statistical analysis, numerical analysis, and symbolic mathematics.
3) It provides information on MATLAB's basic window, help features, GUI, toolboxes including Simulink, and documentation set.
1) The document describes a MATLAB orientation course organized by FOCUS-R&D.
2) The course covers fundamentals of MATLAB including programming basics, plotting, statistical analysis, numerical analysis, and symbolic mathematics.
3) It provides information on MATLAB's basic window, help features, GUI, toolboxes including Simulink, and documentation set.
The document describes a computational study conducted by Ignasi Buch to model the binding process of the ligand benzamidine to the enzyme bovine beta-trypsin. Hundreds of all-atom molecular dynamics simulations were performed to simulate the free ligand binding. The data was analyzed using a Markov state model to describe the system as a network of conformational substates and transitions between them. This allowed quantitative prediction of experimental binding kinetics and a qualitative description of the binding mechanism.
i. The linear convolution of two sequences was calculated using the conv command in MATLAB. The input sequences, individual sequences, and convolved output were plotted.
ii. Linear convolution was also calculated using the DFT and IDFT. The sequences were padded with zeros and transformed to the frequency domain using FFT. The transformed sequences were multiplied and inverse transformed using IFFT to obtain the circular convolution result.
iii. The circular convolution result using DFT/IDFT was the same as the linear convolution using the conv command, demonstrating the equivalence between linear and circular convolution in the frequency domain.
This lab manual covers MATLAB and digital signal processing concepts. It includes:
1) An introduction to MATLAB including basic commands, functions, vectors, matrices and operations.
2) Digital signal processing concepts like sampling, discrete time signals, linear convolution using the conv command are explained.
3) Experiments are included to verify the sampling theorem and study linear convolution of sequences.
TMPA-2015: Implementing the MetaVCG Approach in the C-light SystemIosif Itkin
Alexei Promsky, Dmitry Kondtratyev, A.P. Ershov Institute of Informatics Systems, Novosibirsk
12 - 14 November 2015
Tools and Methods of Program Analysis in St. Petersburg
This document provides a summary of research on solving the simple assembly line balancing problem (SALBP). It begins with an introduction to assembly lines and defining the SALBP. The SALBP involves assigning tasks with precedence constraints and processing times to stations along an assembly line to maximize efficiency. The document then discusses different versions of the SALBP which involve minimizing stations (SALBP-1), minimizing cycle time (SALBP-2), or maximizing efficiency (SALBP-E). It provides an example problem and feasible solution. The remainder of the document surveys solution methods for the different SALBP versions.
Application of combined support vector machines in process fault diagnosisDr.Pooja Jain
This document discusses applying combined support vector machines (C-SVM) for process fault diagnosis and compares its performance to other classifiers. The authors test C-SVM, k-nearest neighbors, and simple SVM on data from the Tennessee Eastman process simulator and a three tank system. Their results show C-SVM achieves the lowest classification error compared to the other methods, though its complexity increases with the number of faults. Principal component analysis did not improve performance over the other classifiers. Selecting important variables using contribution charts significantly enhanced classifier performance on the Tennessee Eastman data.
This document summarizes an experiment using SURF and SIFT algorithms to perform panoramic image stitching. It describes detecting keypoints in images using SURF and SIFT, extracting descriptors, matching features between images, and filtering matches using RANSAC to reject outliers and estimate a fundamental matrix. Homography is also estimated from correspondences to relate point positions between images related by pure rotation. Code examples are provided to detect features and match them using OpenCV.
LEAP is a precise lightweight framework for enterprise architecture modeling. It uses a language-driven approach with simple orthogonal concepts and refinement relationships between layers. Semantics and OCL allow precise analysis of models. A case study demonstrates modeling a university's laptop loan scheme before and after changes. Future work includes expanding modeling capabilities and larger case studies.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
More Related Content
Similar to Fast Evolutionary Optimization: Comparison-Based Optimizers Need Comparison-Based Surrogates. PPSN 2010
Keynote given at the Asia Pacific Software Engineering Conference (APSEC), December 2020, on Automated Program Repair technologies and their applications.
System Verilog 2009 & 2012 enhancementsSubash John
This document summarizes enhancements made to System Verilog in 2009 and 2012. The 2009 enhancements included final blocks, bit selects of expressions, edge detection for DDR logic, fork-join improvements, and display enhancements. The 2012 enhancements extended enums, added scale factors to real constants and mixed-signal assertions, introduced aspect-oriented programming features, and removed X-optimism using new keywords. It also proposed signed operators and discussed some high-level problems not yet addressed.
Ontology mapping requires context, background knowledge, and approximation. Using background knowledge from multiple large ontologies can improve ontology mapping results between two target ontologies by discovering more matches. Exploiting the hierarchical structure in background ontologies through indirect subsumption reasoning can significantly increase the number of matches found. Allowing for approximate matches by introducing a "sloppiness" threshold based on the semantic distance between concepts can further improve results by discovering desirable matches while avoiding undesirable ones except at high sloppiness levels.
The document discusses the double modal transformation technique for analyzing the dynamic response of linear structures subjected to stochastic loading. It proposes a method called double modal transformation that simultaneously transforms the equations of motion and the loading process. This allows the structural response to be obtained through a double series expansion where structural and loading modal contributions are superimposed. The effectiveness of this technique is illustrated through two classic wind engineering problems: alongwind response and vortex-induced crosswind response of slender structures.
MATLAB sessions: Laboratory 2
MAT 275 Laboratory 2
Matrix Computations and Programming in MATLAB
In this laboratory session we will learn how to
1. Create and manipulate matrices and vectors.
2. Write simple programs in MATLAB
NOTE: For your lab write-up, follow the instructions of LAB1.
Matrices and Linear Algebra
⋆ Matrices can be constructed in MATLAB in different ways. For example the 3 × 3 matrix
A =
8 1 63 5 7
4 9 2
can be entered as
>> A=[8,1,6;3,5,7;4,9,2]
A =
8 1 6
3 5 7
4 9 2
or
>> A=[8,1,6;
3,5,7;
4,9,2]
A =
8 1 6
3 5 7
4 9 2
or defined as the concatenation of 3 rows
>> row1=[8,1,6]; row2=[3,5,7]; row3=[4,9,2]; A=[row1;row2;row3]
A =
8 1 6
3 5 7
4 9 2
or 3 columns
>> col1=[8;3;4]; col2=[1;5;9]; col3=[6;7;2]; A=[col1,col2,col3]
A =
8 1 6
3 5 7
4 9 2
Note the use of , and ;. Concatenated rows/columns must have the same length. Larger matrices can
be created from smaller ones in the same way:
c⃝2011 Stefania Tracogna, SoMSS, ASU
MATLAB sessions: Laboratory 2
>> C=[A,A] % Same as C=[A A]
C =
8 1 6 8 1 6
3 5 7 3 5 7
4 9 2 4 9 2
The matrix C has dimension 3 × 6 (“3 by 6”). On the other hand smaller matrices (submatrices) can
be extracted from any given matrix:
>> A(2,3) % coefficient of A in 2nd row, 3rd column
ans =
7
>> A(1,:) % 1st row of A
ans =
8 1 6
>> A(:,3) % 3rd column of A
ans =
6
7
2
>> A([1,3],[2,3]) % keep coefficients in rows 1 & 3 and columns 2 & 3
ans =
1 6
9 2
⋆ Some matrices are already predefined in MATLAB:
>> I=eye(3) % the Identity matrix
I =
1 0 0
0 1 0
0 0 1
>> magic(3)
ans =
8 1 6
3 5 7
4 9 2
(what is magic about this matrix?)
⋆ Matrices can be manipulated very easily in MATLAB (unlike Maple). Here are sample commands
to exercise with:
>> A=magic(3);
>> B=A’ % transpose of A, i.e, rows of B are columns of A
B =
8 3 4
1 5 9
6 7 2
>> A+B % sum of A and B
ans =
16 4 10
4 10 16
10 16 4
>> A*B % standard linear algebra matrix multiplication
ans =
101 71 53
c⃝2011 Stefania Tracogna, SoMSS, ASU
MATLAB sessions: Laboratory 2
71 83 71
53 71 101
>> A.*B % coefficient-wise multiplication
ans =
64 3 24
3 25 63
24 63 4
⋆ One MATLAB command is especially relevant when studying the solution of linear systems of dif-
ferentials equations: x=A\b determines the solution x = A−1b of the linear system Ax = b. Here is an
example:
>> A=magic(3);
>> z=[1,2,3]’ % same as z=[1;2;3]
z =
1
2
3
>> b=A*z
b =
28
34
28
>> x = A\b % solve the system Ax = b. Compare with the exact solution, z, defined above.
x =
1
2
3
>> y =inv(A)*b % solve the system using the inverse: less efficient and accurate
ans =
1.0000
2.0000
3.0000
Now let’s check for accuracy by evaluating the difference z − x and z − y. In exact arithmetic they
should both be zero since x, y and z all represent the solution to the system.
>> z - x % error for backslash command
ans =
0
0
0
>> z - y % error for inverse
ans =
1.0e-015 *
-0.4441
0
-0.88 ...
This document provides an overview of an introductory course on using R for statistical analysis. It covers topics such as the R environment and language, working with objects and data types, importing and manipulating data, and performing basic analyses and visualizations. The course materials are divided into sections covering the R workspace, reading and writing data, data manipulation, plotting, and more advanced techniques. Examples are provided throughout to demonstrate key R functions and capabilities.
1) The document describes a MATLAB orientation course organized by FOCUS-R&D.
2) The course covers fundamentals of MATLAB including programming basics, plotting, statistical analysis, numerical analysis, and symbolic mathematics.
3) It provides information on MATLAB's basic window, help features, GUI, toolboxes including Simulink, and documentation set.
1) The document describes a MATLAB orientation course organized by FOCUS-R&D.
2) The course covers fundamentals of MATLAB including programming basics, plotting, statistical analysis, numerical analysis, and symbolic mathematics.
3) It provides information on MATLAB's basic window, help features, GUI, toolboxes including Simulink, and documentation set.
The document describes a computational study conducted by Ignasi Buch to model the binding process of the ligand benzamidine to the enzyme bovine beta-trypsin. Hundreds of all-atom molecular dynamics simulations were performed to simulate the free ligand binding. The data was analyzed using a Markov state model to describe the system as a network of conformational substates and transitions between them. This allowed quantitative prediction of experimental binding kinetics and a qualitative description of the binding mechanism.
i. The linear convolution of two sequences was calculated using the conv command in MATLAB. The input sequences, individual sequences, and convolved output were plotted.
ii. Linear convolution was also calculated using the DFT and IDFT. The sequences were padded with zeros and transformed to the frequency domain using FFT. The transformed sequences were multiplied and inverse transformed using IFFT to obtain the circular convolution result.
iii. The circular convolution result using DFT/IDFT was the same as the linear convolution using the conv command, demonstrating the equivalence between linear and circular convolution in the frequency domain.
This lab manual covers MATLAB and digital signal processing concepts. It includes:
1) An introduction to MATLAB including basic commands, functions, vectors, matrices and operations.
2) Digital signal processing concepts like sampling, discrete time signals, linear convolution using the conv command are explained.
3) Experiments are included to verify the sampling theorem and study linear convolution of sequences.
TMPA-2015: Implementing the MetaVCG Approach in the C-light SystemIosif Itkin
Alexei Promsky, Dmitry Kondtratyev, A.P. Ershov Institute of Informatics Systems, Novosibirsk
12 - 14 November 2015
Tools and Methods of Program Analysis in St. Petersburg
This document provides a summary of research on solving the simple assembly line balancing problem (SALBP). It begins with an introduction to assembly lines and defining the SALBP. The SALBP involves assigning tasks with precedence constraints and processing times to stations along an assembly line to maximize efficiency. The document then discusses different versions of the SALBP which involve minimizing stations (SALBP-1), minimizing cycle time (SALBP-2), or maximizing efficiency (SALBP-E). It provides an example problem and feasible solution. The remainder of the document surveys solution methods for the different SALBP versions.
Application of combined support vector machines in process fault diagnosisDr.Pooja Jain
This document discusses applying combined support vector machines (C-SVM) for process fault diagnosis and compares its performance to other classifiers. The authors test C-SVM, k-nearest neighbors, and simple SVM on data from the Tennessee Eastman process simulator and a three tank system. Their results show C-SVM achieves the lowest classification error compared to the other methods, though its complexity increases with the number of faults. Principal component analysis did not improve performance over the other classifiers. Selecting important variables using contribution charts significantly enhanced classifier performance on the Tennessee Eastman data.
This document summarizes an experiment using SURF and SIFT algorithms to perform panoramic image stitching. It describes detecting keypoints in images using SURF and SIFT, extracting descriptors, matching features between images, and filtering matches using RANSAC to reject outliers and estimate a fundamental matrix. Homography is also estimated from correspondences to relate point positions between images related by pure rotation. Code examples are provided to detect features and match them using OpenCV.
LEAP is a precise lightweight framework for enterprise architecture modeling. It uses a language-driven approach with simple orthogonal concepts and refinement relationships between layers. Semantics and OCL allow precise analysis of models. A case study demonstrates modeling a university's laptop loan scheme before and after changes. Future work includes expanding modeling capabilities and larger case studies.
Similar to Fast Evolutionary Optimization: Comparison-Based Optimizers Need Comparison-Based Surrogates. PPSN 2010 (18)
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
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Information and Communication Technology in EducationMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 2)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐈𝐂𝐓 𝐢𝐧 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
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4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
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3. Motivation
Why Comparison-Based Surrogates ?
Background
Previous Work
ACM-ES
Why Comparison-Based Surrogates ?
Surrogate Model (Meta-Model) assisted optimization
Construct the approximation model M (x) of f (x).
Optimize the model M (x) in lieu of f (x) to reduce
the number of costly evaluations of the function f (x).
Example
f (x) = x2 + (x1 + x2 )2 .
1
An efficient Evolutionary Algorithm (EA) with
surrogate models may be 4.3 faster on f (x).
But the same EA is only 2.4 faster on f (x) = f (x)1/4 ! a
a CMA-ES with quadratic meta-model (lmm-CMA-ES) on fSchwefel 2-D
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 3/ 20
4. Motivation
Why Comparison-Based Surrogates ?
Background
Previous Work
ACM-ES
Ordinal Regression in Evolutionary Computation
Goal: Find the function F (x) which preserves the ordering of the
training points xi (xi has rank i):
xi xj ⇔ F (xi ) > F (xj )
F (x) is invariant to any rank-preserving transformation.
CMA-ES with Rank Support Vector Machine on Rosenbrock: 1
n =2 2
n =5 3
n =10
10 10
0
10
( I ,λ )CMA-ES
Poly, d =2
mean fitness
10
1
10
2 Poly, d =4
2
10
RBF, γ =1
0 1
4 10 10
10
6 1 0
10 10 10
200 400 600 200 600 1000 1000 2000 3000
# function evaluations # function evaluations # function evaluations
1 T. Runarsson (2006). "Ordinal Regression in Evolutionary Computation"
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 4/ 20
5. Motivation
Why Comparison-Based Surrogates ?
Background
Previous Work
ACM-ES
Exploit the local topography of the function
CMA-ES adapts the covariance matrix C which describes the
local structure of the function.
Mahalanobis (fully weighted Euclidean) distance:
d(xi , xj ) = (xi − xj )T C −1 (xi − xj )
2
Results of CMA-ES with quadratic meta-models:
8
10 lmm-CMA-ES
O(FLOP)/saved function evaluation
Speed-up: a factor of 2-4 for n ≥ 4
6
10
Complexity: from O(n4 ) to O(n6 )
4
10 Rank-preserving invariance: NO
becomes intractable for n>16
2
10
2 4 8 16
n
2 S. Kern et al. (2006). "Local Meta-Models for Optimization Using Evolution Strategies"
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 5/ 20
6. Motivation
Why Comparison-Based Surrogates ?
Background
Previous Work
ACM-ES
Tractable or Efficient ?
Answer: Tractable and Efficient and Invariant.
Ingredients: CMA-ES (Adaptive Encoding) and Rank SVM.
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 6/ 20
7. Motivation
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Background
Support Vector Machine (SVM)
ACM-ES
Covariance Matrix Adaptation Evolution Strategy
Decompose to understand
While CMA-ES by definition is CMA and ES, only recently the
algorithmic decomposition has been presented. 3
Algorithm 1 CMA-ES = Adaptive Encoding + ES
1: xi ← m + σNi (0, I), for i = 1 . . . λ
2: fi ← f (Bxi ), for i = 1 . . . λ
3: if Evolution Strategy (ES) then
success rate
4: σ ← σexp∝( expected success rate −1)
5: if Cumulative Step-Size Adaptation ES (CSA-ES) then
evolution path
6: σ ← σexp∝( expected evolution path −1)
7: B ←AECMA -Update(Bx1 , . . . , Bxµ )
3 N. Hansen (2008). "Adaptive Encoding: How to Render Search Coordinate System Invariant"
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 7/ 20
8. Motivation
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Background
Support Vector Machine (SVM)
ACM-ES
Adaptive Encoding
Inspired by Principal Component Analysis (PCA)
Principal Component Analysis
Adaptive Encoding Update
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 8/ 20
9. Motivation
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Background
Support Vector Machine (SVM)
ACM-ES
Support Vector Machine for Classification
Linear Classifier
L1 Main Idea
L2
Training Data:
w n
L3 D = {(xi , yi )|xi ∈ I p , yi ∈ {−1, +1}}i=1
R
w, xi ≥ b + ⇒ yi = +1;
w, xi ≤ b − ⇒ yi = −1;
Dividing by > 0:
b w, xi − b ≥ +1 ⇒ yi = +1;
+1 w, xi − b ≤ −1 ⇒ yi = −1;
b 0
b -1
support b Optimization Problem: Primal Form
vector w
n
Minimize{w, ξ} 1 ||w||2 + C i=1 ξi
2
xi subject to: yi ( w, xi − b) ≥ 1 − ξi , ξi ≥ 0
2/ ||w||
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 9/ 20
10. Motivation
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Background
Support Vector Machine (SVM)
ACM-ES
Support Vector Machine for Classification
Linear Classifier
L1 Optimization Problem: Dual Form
L2
From Lagrange Theorem, instead of minimize F :
w
L3 Minimize{α,G} F − i αi Gi
subject to: αi ≥ 0, Gi ≥ 0
Leaving the details, Dual form:
n 1 n
Maximize{α} i αi − 2 i,j=1 αi αj yi yj xi , xj
b n
subject to: 0 ≤ αi ≤ C, i αi yi = 0
+1
b 0
b -1 Properties
support b
vector w Decision Function:
n
F (x) = sign( i αi yi xi , x − b)
xi
The Dual form may be solved using standard
2/ ||w|| quadratic programming solver.
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 10/ 20
11. Motivation
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Background
Support Vector Machine (SVM)
ACM-ES
Support Vector Machine for Classification
Non-Linear Classifier
w,F(x) -b = +1 w
F
w,F(x) -b = -1 xi
support vector
2/ ||w||
a) b) c)
Non-linear classification with the "Kernel trick"
1n n
Maximize{α} i αi − 2 i,j=1 αi αj yi yj K(xi , xj )
n
subject to: ai ≥ 0, i αi yi = 0,
where K(x, x ) =def < Φ(x), Φ(x ) > is the Kernel function
n
Decision Function: F (x) = sign( i αi yi K(xi , x) − b)
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 11/ 20
12. Motivation
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Background
Support Vector Machine (SVM)
ACM-ES
Support Vector Machine for Classification
Non-Linear Classifier: Kernels
Polynomial: k(xi , xj ) = ( xi , xj + 1)d
2
xi −xj
Gaussian or Radial Basis Function: k(xi , xj ) = exp( 2σ 2 )
Hyperbolic tangent: k(xi , xj ) = tanh(k xi , xj + c)
Examples for Polynomial (left) and Gaussian (right) Kernels:
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 12/ 20
13. Motivation
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Background
Support Vector Machine (SVM)
ACM-ES
Ranking Support Vector Machine
Find F (x) which preserves the ordering of the training points.
w
x x
r2)
L(
x
r1)
L(
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 13/ 20
14. Motivation
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Background
Support Vector Machine (SVM)
ACM-ES
Ranking Support Vector Machine
Primal problem
N
Minimize{w, ξ} 1 ||w||2 +
2 i=1 C i ξi
w, Φ(xi ) − Φ(xi+1 ) ≥ 1 − ξi (i = 1 . . . N − 1)
subject to
ξi ≥ 0 (i = 1 . . . N − 1)
Dual problem
N −1 N −1
Maximize{α} i αi − i,j αij K(xi − xi+1 , xj − xj+1 ))
subject to 0 ≤ αi ≤ Ci (i = 1 . . . N − 1)
Rank Surrogate Function in the case 1 rank = 1 point
N −1
F(x) = i=1 αi (K(xi , x) − K(xi+1 , x))
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 14/ 20
16. Motivation
Algorithm
Background
Experiments
ACM-ES
Optimization or filtering?
Don’t be too greedy
Optimization: Significant Potential Speed-Up if the surrogate
model is global and accurate enough
Filtering: "Guaranteed" Speed-Up with the local surrogate model
Prescreen
(λ ) Retain with rank , ~
Evaluate
Retain with rank λ, ~λ
(λ′ )
0.6
Probability Density
0.5
0.4
0.3
0.2
0 100 200 300 400 500
Rank
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 16/ 20
17. Motivation
Algorithm
Background
Experiments
ACM-ES
ACM-ES Optimization Loop
2. The change of coordinates, defined
from the current covariance matrix
A. Select training points and the current mean value , reads [4]:
B. Build a surrogate model
1. Select best k training points.
1 1
0.5 0.5
X2
X2
0 0
-0.5 -0.5
-1 -1
3. Build a surrogate model using Rank SVM.
-1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1
X1 X1
7. Add new λ′ training points and
update parameters of CMA-ES.
Rank-based
Surrogate
D. Select most promising children C. Generate pre-children
Model
5. Prescreen 4. Generate pre-children and rank them
(λ ) Retain with rank , ~
according to surrogate fitness function.
6. Evaluate
Retain with rank λ, ~λ
(λ′ )
0.6
Probability Density
0.5
0.4
0.3
0.2
0 100 200 300 400 500
Rank
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 17/ 20
19. Motivation
Algorithm
Background
Experiments
ACM-ES
Results
Learning Time
Cost of model learning/testing increases
quasi-linearly with d on Sphere function:
10
Slope=1.13
Learning time (ms)
3
10
2
10
1
10
0 1 2 3
10 10 10 10
Problem Dimension
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 19/ 20
20. Motivation
Algorithm
Background
Experiments
ACM-ES
Summary
ACM-ES
ACM-ES is from 2 to 4 times faster on Uni-Modal Problems.
Invariant to rank-preserving transformation: Yes
The computation complexity (the cost of speed-up) is O(n)
comparing to O(n6 )
The source code is available online:
http://www.lri.fr/~ilya/publications/ACMESppsn2010.zip
Open Questions
Extention to multi-modal optimization
Adaptation of selection pressure and surrogate model complexity
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag ACM-ES = CMA-ES + RankSVM 20/ 20
21. Motivation
Algorithm
Background
Experiments
ACM-ES
Summary
Thank you for your attention!
Questions?
22. Motivation
Algorithm
Background
Experiments
ACM-ES
Parameters
SVM Learning:
√
Number of training points: Ntraining = 30 d for all problems,
except Rosenbrock and Rastrigin, where Ntraining = 70 (d)
√
Number of iterations: Niter = 50000 d
Kernel function: RBF function with σ equal to the average
distance of the training points
The cost of constraint violation: Ci = 106 (Ntraining − i)2.0
Offspring Selection Procedure:
Number of test points: Ntest = 500
Number of evaluated offsprings: λ = λ3
2 2
Offspring selection pressure parameters: σsel0 = 2σsel1 = 0.8