Chapter 4 algorithmic efficiency

•

0 likes•62 views

M

This chapter discusses algorithmic complexity and efficiency by making assumptions that all primitive operations take the same time and complex operations cost is the sum of primitive operations. It introduces best case, average case, and worst case complexity and mentions exercises to test understanding of algorithmic complexity.

Chapter 4
Algorithmic Complexity/Eﬃciency

Assumptions:
• All primitive operations take the
same time
• The cost of complex operations
is the sum of their primitive
operations

Best case?
Average case?
Worst case?

= n

Thats it!
Now it is time to do the
exercises to test that
you now understand
algorithmic complexity

Recommended

Chapter 9 divide and conquer handouts with notes

Chapter 9 divide and conquer handouts with notes

Chapter 9 divide and conquer handouts with notes

Chapter 9 divide and conquer handouts

Chapter 9 divide and conquer handouts

Chapter 9 divide and conquer handouts

Chapter 9 divide and conquer

Chapter 9 divide and conquer

Chapter 9 divide and conquer

Chapter 7 recursion handouts with notes

Chapter 7 recursion handouts with notes

Chapter 7 recursion handouts with notes

Chapter 7 recursion handouts

Chapter 7 recursion handouts

Chapter 7 recursion handouts

Chapter 7 recursion

Chapter 7 recursion

Chapter 7 recursion

Chapter 5 searching and sorting handouts with notes

Chapter 5 searching and sorting handouts with notes

Chapter 5 searching and sorting handouts with notes

Chapter 5 searching and sorting handouts

Chapter 5 searching and sorting handouts

Chapter 5 searching and sorting handouts

Recommended

Chapter 9 divide and conquer handouts with notes

Chapter 9 divide and conquer handouts with notes

Chapter 9 divide and conquer handouts with notes

Chapter 9 divide and conquer handouts

Chapter 9 divide and conquer handouts

Chapter 9 divide and conquer handouts

Chapter 9 divide and conquer

Chapter 9 divide and conquer

Chapter 9 divide and conquer

Chapter 7 recursion handouts with notes

Chapter 7 recursion handouts with notes

Chapter 7 recursion handouts with notes

Chapter 7 recursion handouts

Chapter 7 recursion handouts

Chapter 7 recursion handouts

Chapter 7 recursion

Chapter 7 recursion

Chapter 7 recursion

Chapter 5 searching and sorting handouts with notes

Chapter 5 searching and sorting handouts with notes

Chapter 5 searching and sorting handouts with notes

Chapter 5 searching and sorting handouts

Chapter 5 searching and sorting handouts

Chapter 5 searching and sorting handouts

Chapter 5 searching and sorting

Chapter 5 searching and sorting

Chapter 5 searching and sorting

Chapter 4 algorithmic efficiency handouts (with notes)

Chapter 4 algorithmic efficiency handouts (with notes)

Chapter 4 algorithmic efficiency handouts (with notes)

Chapter 4 algorithmic efficiency handouts

Chapter 4 algorithmic efficiency handouts

Chapter 4 algorithmic efficiency handouts

Chapter 3 introduction to algorithms slides

Chapter 3 introduction to algorithms slides

Chapter 3 introduction to algorithms slides

Chapter 3 introduction to algorithms handouts (with notes)

Chapter 3 introduction to algorithms handouts (with notes)

Chapter 3 introduction to algorithms handouts (with notes)

Chapter 3 introduction to algorithms handouts

Chapter 3 introduction to algorithms handouts

Chapter 3 introduction to algorithms handouts

Ku 05 08 2009mailund

Association mapping using local genealogies

Association mapping using local genealogies

Association mapping using local genealogiesmailund

Neural Networks

Neural Networks

Neural Networksmailund

Probability And Stats Intro

Probability And Stats Intro

Probability And Stats Intromailund

Probability And Stats Intro2

Probability And Stats Intro2

Probability And Stats Intro2mailund

Linear Regression Ex

Linear Regression Ex

Linear Regression Exmailund

Course Introduction

Course Introduction

Course Introductionmailund

Linear Classification

Linear Classification

Linear Classificationmailund

Linear Regression

Linear Regression

Linear Regressionmailund

Presentation at APBC 2007

Presentation at APBC 2007

Presentation at APBC 2007mailund

Epidemiologisk FredagsmøDe 15 2 2008

Epidemiologisk FredagsmøDe 15 2 2008

Epidemiologisk FredagsmøDe 15 2 2008mailund

Monkey Business

Monkey Business

Monkey Business

Wu Mamber (String Algorithms 2007)

Wu Mamber (String Algorithms 2007)

Wu Mamber (String Algorithms 2007)

Approximate Matching (String Algorithms 2007)

Approximate Matching (String Algorithms 2007)

Approximate Matching (String Algorithms 2007)

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

AADYARAJPANDEY1

Introduction: RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression. It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences. dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants. This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes. What are small ncRNAs? micro RNA (miRNA) short interfering RNA (siRNA) Properties of small non-coding RNA: Involved in silencing mRNA transcripts. Called “small” because they are usually only about 21-24 nucleotides long. Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered). Silence an mRNA by base pairing with some sequence on the mRNA. Discovery of siRNA? The first small RNA: In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the development of the worm C. elegans. Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14. Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions. Types of RNAi ( non coding RNA) MiRNA Length (23-25 nt) Trans acting Binds with target MRNA in mismatch Translation inhibition Si RNA Length 21 nt. Cis acting Bind with target Mrna in perfect complementary sequence Piwi-RNA Length ; 25 to 36 nt. Expressed in Germ Cells Regulates trnasposomes activity MECHANISM OF RNAI: First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces. Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands. The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it. THE RISC COMPLEX: RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA Unwinding of double stranded Si RNA by ATP independent Helicase Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA. DICER: endonuclease (RNase Family III) Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC) One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute ARGONAUTE PROTEIN : 1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA 2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity. MiRNA: The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .

Predicting property prices with machine learning algorithms.pdf

Predicting property prices with machine learning algorithms.pdf

Predicting property prices with machine learning algorithms.pdf

More Related Content

More from mailund

Chapter 5 searching and sorting

Chapter 5 searching and sorting

Chapter 5 searching and sorting

Chapter 4 algorithmic efficiency handouts (with notes)

Chapter 4 algorithmic efficiency handouts (with notes)

Chapter 4 algorithmic efficiency handouts (with notes)

Chapter 4 algorithmic efficiency handouts

Chapter 4 algorithmic efficiency handouts

Chapter 4 algorithmic efficiency handouts

Chapter 3 introduction to algorithms slides

Chapter 3 introduction to algorithms slides

Chapter 3 introduction to algorithms slides

Chapter 3 introduction to algorithms handouts (with notes)

Chapter 3 introduction to algorithms handouts (with notes)

Chapter 3 introduction to algorithms handouts (with notes)

Chapter 3 introduction to algorithms handouts

Chapter 3 introduction to algorithms handouts

Chapter 3 introduction to algorithms handouts

Ku 05 08 2009mailund

Association mapping using local genealogies

Association mapping using local genealogies

Association mapping using local genealogiesmailund

Neural Networks

Neural Networks

Neural Networksmailund

Probability And Stats Intro

Probability And Stats Intro

Probability And Stats Intromailund

Probability And Stats Intro2

Probability And Stats Intro2

Probability And Stats Intro2mailund

Linear Regression Ex

Linear Regression Ex

Linear Regression Exmailund

Course Introduction

Course Introduction

Course Introductionmailund

Linear Classification

Linear Classification

Linear Classificationmailund

Linear Regression

Linear Regression

Linear Regressionmailund

Presentation at APBC 2007

Presentation at APBC 2007

Presentation at APBC 2007mailund

Epidemiologisk FredagsmøDe 15 2 2008

Epidemiologisk FredagsmøDe 15 2 2008

Epidemiologisk FredagsmøDe 15 2 2008mailund

Monkey Business

Monkey Business

Monkey Business

Wu Mamber (String Algorithms 2007)

Wu Mamber (String Algorithms 2007)

Wu Mamber (String Algorithms 2007)

Approximate Matching (String Algorithms 2007)

Approximate Matching (String Algorithms 2007)

Approximate Matching (String Algorithms 2007)

More from mailund (20)

Chapter 5 searching and sorting

Chapter 5 searching and sorting

Chapter 5 searching and sorting

Chapter 4 algorithmic efficiency handouts (with notes)

Chapter 4 algorithmic efficiency handouts (with notes)

Chapter 4 algorithmic efficiency handouts (with notes)

Chapter 4 algorithmic efficiency handouts

Chapter 4 algorithmic efficiency handouts

Chapter 4 algorithmic efficiency handouts

Chapter 3 introduction to algorithms slides

Chapter 3 introduction to algorithms slides

Chapter 3 introduction to algorithms slides

Chapter 3 introduction to algorithms handouts (with notes)

Chapter 3 introduction to algorithms handouts (with notes)

Chapter 3 introduction to algorithms handouts (with notes)

Chapter 3 introduction to algorithms handouts

Chapter 3 introduction to algorithms handouts

Chapter 3 introduction to algorithms handouts

Ku 05 08 2009

Association mapping using local genealogies

Association mapping using local genealogies

Association mapping using local genealogies

Neural Networks

Neural Networks

Neural Networks

Probability And Stats Intro

Probability And Stats Intro

Probability And Stats Intro

Probability And Stats Intro2

Probability And Stats Intro2

Probability And Stats Intro2

Linear Regression Ex

Linear Regression Ex

Linear Regression Ex

Course Introduction

Course Introduction

Course Introduction

Linear Classification

Linear Classification

Linear Classification

Linear Regression

Linear Regression

Linear Regression

Presentation at APBC 2007

Presentation at APBC 2007

Presentation at APBC 2007

Epidemiologisk FredagsmøDe 15 2 2008

Epidemiologisk FredagsmøDe 15 2 2008

Epidemiologisk FredagsmøDe 15 2 2008

Monkey Business

Monkey Business

Monkey Business

Wu Mamber (String Algorithms 2007)

Wu Mamber (String Algorithms 2007)

Wu Mamber (String Algorithms 2007)

Approximate Matching (String Algorithms 2007)

Approximate Matching (String Algorithms 2007)

Approximate Matching (String Algorithms 2007)

Recently uploaded

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

AADYARAJPANDEY1

Introduction: RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression. It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences. dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants. This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes. What are small ncRNAs? micro RNA (miRNA) short interfering RNA (siRNA) Properties of small non-coding RNA: Involved in silencing mRNA transcripts. Called “small” because they are usually only about 21-24 nucleotides long. Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered). Silence an mRNA by base pairing with some sequence on the mRNA. Discovery of siRNA? The first small RNA: In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the development of the worm C. elegans. Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14. Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions. Types of RNAi ( non coding RNA) MiRNA Length (23-25 nt) Trans acting Binds with target MRNA in mismatch Translation inhibition Si RNA Length 21 nt. Cis acting Bind with target Mrna in perfect complementary sequence Piwi-RNA Length ; 25 to 36 nt. Expressed in Germ Cells Regulates trnasposomes activity MECHANISM OF RNAI: First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces. Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands. The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it. THE RISC COMPLEX: RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA Unwinding of double stranded Si RNA by ATP independent Helicase Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA. DICER: endonuclease (RNase Family III) Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC) One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute ARGONAUTE PROTEIN : 1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA 2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity. MiRNA: The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .

Predicting property prices with machine learning algorithms.pdf

Predicting property prices with machine learning algorithms.pdf

Predicting property prices with machine learning algorithms.pdf

THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.

THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.

THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.

The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.

justice-and-fairness-ethics with example

justice-and-fairness-ethics with example

justice-and-fairness-ethics with example

Orion Air Quality Monitoring Systems - CWS

Orion Air Quality Monitoring Systems - CWS

Orion Air Quality Monitoring Systems - CWS

Columbia Weather Systems

Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...

Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...

Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...

We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and 30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1 . Our search finds no candidates at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to infer the properties of the evolving luminosity function without binning in redshift or luminosity that marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results, and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5 from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical models for evolution of the dark matter halo mass function.

Cancer cell metabolism: special Reference to Lactate Pathway

Cancer cell metabolism: special Reference to Lactate Pathway

Cancer cell metabolism: special Reference to Lactate Pathway

AADYARAJPANDEY1

Normal Cell Metabolism: Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function. Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released. Cell utilize energy in the form of ATP. The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process. Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP. The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos). It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation. If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use. IN CANCER CELL: Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful. Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation. This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive. Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful. Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation. This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive. introduction to WARBERG PHENOMENA: WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside. Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme. WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.

Viksit bharat till 2047 India@2047.pptx

Viksit bharat till 2047 India@2047.pptx

Viksit bharat till 2047 India@2047.pptx

rakeshsharma20142015

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

Seminar of U.V. Spectroscopy by SAMIR PANDA

Seminar of U.V. Spectroscopy by SAMIR PANDA

Seminar of U.V. Spectroscopy by SAMIR PANDA

ESR_factors_affect-clinic significance-Pathysiology.pptx

ESR_factors_affect-clinic significance-Pathysiology.pptx

ESR_factors_affect-clinic significance-Pathysiology.pptx

Astronomy Update- Curiosity’s exploration of Mars _ Local Briefs _ leadertele...

Astronomy Update- Curiosity’s exploration of Mars _ Local Briefs _ leadertele...

Astronomy Update- Curiosity’s exploration of Mars _ Local Briefs _ leadertele...

NathanBaughman3

FAIR & AI Ready KGs for Explainable Predictions

FAIR & AI Ready KGs for Explainable Predictions

FAIR & AI Ready KGs for Explainable Predictions

Michel Dumontier

The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed. In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine. Bio Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.

plant biotechnology Lecture note ppt.pptx

plant biotechnology Lecture note ppt.pptx

plant biotechnology Lecture note ppt.pptx

Hemostasis_importance& clinical significance.pptx

Hemostasis_importance& clinical significance.pptx

Hemostasis_importance& clinical significance.pptx

Unveiling the Energy Potential of Marshmallow Deposits.pdf

Unveiling the Energy Potential of Marshmallow Deposits.pdf

Unveiling the Energy Potential of Marshmallow Deposits.pdf

Erdal Coalmaker

filosofia boliviana introducción jsjdjd.pptx

filosofia boliviana introducción jsjdjd.pptx

filosofia boliviana introducción jsjdjd.pptx

Circulatory system_ Laplace law. Ohms law.reynaults law,baro-chemo-receptors-...

Circulatory system_ Laplace law. Ohms law.reynaults law,baro-chemo-receptors-...

Circulatory system_ Laplace law. Ohms law.reynaults law,baro-chemo-receptors-...

In silico drugs analogue design: novobiocin analogues.pptx

In silico drugs analogue design: novobiocin analogues.pptx

In silico drugs analogue design: novobiocin analogues.pptx

The ASGCT Annual Meeting was packed with exciting progress in the field advan...

The ASGCT Annual Meeting was packed with exciting progress in the field advan...

The ASGCT Annual Meeting was packed with exciting progress in the field advan...

Health Advances

Recently uploaded (20)

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

Predicting property prices with machine learning algorithms.pdf

Predicting property prices with machine learning algorithms.pdf

Predicting property prices with machine learning algorithms.pdf

THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.

THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.

THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.

justice-and-fairness-ethics with example

justice-and-fairness-ethics with example

justice-and-fairness-ethics with example

Orion Air Quality Monitoring Systems - CWS

Orion Air Quality Monitoring Systems - CWS

Orion Air Quality Monitoring Systems - CWS

Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...

Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...

Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...

Cancer cell metabolism: special Reference to Lactate Pathway

Cancer cell metabolism: special Reference to Lactate Pathway

Cancer cell metabolism: special Reference to Lactate Pathway

Viksit bharat till 2047 India@2047.pptx

Viksit bharat till 2047 India@2047.pptx

Viksit bharat till 2047 India@2047.pptx

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

Seminar of U.V. Spectroscopy by SAMIR PANDA

Seminar of U.V. Spectroscopy by SAMIR PANDA

Seminar of U.V. Spectroscopy by SAMIR PANDA

ESR_factors_affect-clinic significance-Pathysiology.pptx

ESR_factors_affect-clinic significance-Pathysiology.pptx

ESR_factors_affect-clinic significance-Pathysiology.pptx

Astronomy Update- Curiosity’s exploration of Mars _ Local Briefs _ leadertele...

Astronomy Update- Curiosity’s exploration of Mars _ Local Briefs _ leadertele...

Astronomy Update- Curiosity’s exploration of Mars _ Local Briefs _ leadertele...

FAIR & AI Ready KGs for Explainable Predictions

FAIR & AI Ready KGs for Explainable Predictions

FAIR & AI Ready KGs for Explainable Predictions

plant biotechnology Lecture note ppt.pptx

plant biotechnology Lecture note ppt.pptx

plant biotechnology Lecture note ppt.pptx

Hemostasis_importance& clinical significance.pptx

Hemostasis_importance& clinical significance.pptx

Hemostasis_importance& clinical significance.pptx

Unveiling the Energy Potential of Marshmallow Deposits.pdf

Unveiling the Energy Potential of Marshmallow Deposits.pdf

Unveiling the Energy Potential of Marshmallow Deposits.pdf

filosofia boliviana introducción jsjdjd.pptx

filosofia boliviana introducción jsjdjd.pptx

filosofia boliviana introducción jsjdjd.pptx

Circulatory system_ Laplace law. Ohms law.reynaults law,baro-chemo-receptors-...

Circulatory system_ Laplace law. Ohms law.reynaults law,baro-chemo-receptors-...

Circulatory system_ Laplace law. Ohms law.reynaults law,baro-chemo-receptors-...

In silico drugs analogue design: novobiocin analogues.pptx

In silico drugs analogue design: novobiocin analogues.pptx

In silico drugs analogue design: novobiocin analogues.pptx

The ASGCT Annual Meeting was packed with exciting progress in the field advan...

The ASGCT Annual Meeting was packed with exciting progress in the field advan...

The ASGCT Annual Meeting was packed with exciting progress in the field advan...

Chapter 4 algorithmic efficiency

1. Chapter 4 Algorithmic Complexity/Eﬃciency

2.

3.

4.

5.

6.

7.

8.

9.

10. Assumptions: • All primitive operations take the same time • The cost of complex operations is the sum of their primitive operations

11.

12.

13.

14.

15.

16.

17.

18.

19.

20. Best case? Average case? Worst case?

22.

23.

24.

25.

26.

27. Thats it! Now it is time to do the exercises to test that you now understand algorithmic complexity