text:
# magnesium oxide balanced equation

The balanced equation will appear above. Compound states [like (s) (aq) or (g)] are not required. You can specify conditions of storing and accessing cookies in your browser, Write a balanced chemical equation when magnesium oxide is dissolved in water, CH₃-CO-CH₂-CH₃ ,choose the correct IUPAC name., Koi meri lovi sis ke answers thank kar sakta haiBhaiya I know taran didi bhaut tension leti hain sabki aap maaf mat karna Satyajit ko jinki hum sabse When an element reacts with a compound, a single displacement reaction often occurs. Solution for Write a balanced chemical equation for the following: magnesium metal is burned in oxygen to produce magnesium oxide. The resulting layer of magnesium oxide can interfere with experimental results. Be sure to count all of the oxygen atoms on the reactant (left) side of the equation. Magnesium iodide has few commercial uses, but can be used to prepare compounds for organic synthesis.Died by water {WASTED} Preparation. Mg(s) is limiting reaction Determine the theoretical yield for the reaction. Write a balanced equation for the reaction of {eq}Mg {/eq} with {eq}HCI_{(aq)} {/eq}. The balanced equation will appear above. Learn more about balanced chemical equation: This site is using cookies under cookie policy. The balanced chemical equation for the reaction of magnesium oxide with water follows: 1 mole of magnesium oxide reacts with 1 mole of water to produce 1 mole of. It is a principal fireproofing ingredient in construction materials. {/eq}. A balanced chemical equation is defined as the equation in which total number of individual atoms on the reactant side is equal to the total number of individual atoms on product side. {/eq} with {eq}HCI_{(aq)} The balanced equation for the reaction is 2Mg(s)+O2(g)→2MgO(s) When 10.2g Mg is allowed to react with 10.5g O2, 12.0g MgO is collected. Label all reactants and products in the equations above. The balanced equation for the reaction is: 2Mg(s)+O2(g)→2MgO(s) When 10.1 g of Mg are allowed to react with 10.5 g of O2, 11.8 g of MgO are collected. By referring to an activity series, we can see that magnesium (Mg) is more reactive then hydrogen (H) and therefore will displace it. a) Write a balanced chemical equation for the above reaction. Magnesium Reactivity: Pure magnesium is a highly reactive metal and will react with oxygen if exposed to the air. All rights reserved. Our experts can answer your tough homework and study questions. Example: 2Mg + O2 -> 2MgO Magnesium and oxygen combine to form the compound magnesium oxide. …, zyada care karte hain vahi hamare saath aisa karte hainTaran didi ko theek hona padega bhaiya, Discuss the anamelous behaviour of berilium, Difference between metals and non - metals , Explain why ionisation energy of nitrogen is greater than carbon, Hello friends please pray for our taran sis please friends sab pray karo taran sis ke liye unki haalat bhaut kharab hai please friends taran sis ke li The balanced chemical equation is . Determine the percent yield for the reaction. A reaction where oxygen is added is called an . All other trademarks and copyrights are the property of their respective owners. Rust is a form of iron oxide and it forms slowly when iron is exposed to air. MgO + 2 HI → MgI 2 + H 2 O Mg(OH) 2 + 2 HI → MgI 2 + 2 H 2 O MgCO 3 + 2 HI → MgI 2 + CO 2 + H 2 O For example, C6H5C2H5 + O2 = C6H5OH + CO2 + H2O will not be balanced, but XC2H5 + O2 = XOH + CO2 + H2O will. Determine the limiting reactant for the reaction. This reaction takes place in cold water. So the balanced equation is 2 Mg + O 2 2 MgO.. All atoms are now balanced in the reaction (put mouse arrow over the above reaction to see the result).. Use uppercase for the first character in the element and lowercase for the second character. Example. The balanced equation is therefore: {eq}\rm Mg(s) + 2HCl(aq) \to MgCl_2(aq) + H_2(g) Magnesium reacts with oxygen to produce magnesium oxide. (Express your answer as a chemical formula.) Example: Iron + Oxygen → Iron(III) oxide 4Fe(s) + 3O 2(g) → 2Fe 2O3(s) 4 atoms Fe + 3 molecules O 2 → 2 formula units Fe 2O3 4 moles Fe + 3 moles O 2 → 2 moles Fe 2O3 223.40 g + 96.00 g → 319.40 g Uses. How much does does a 100 dollar roblox gift card get you in robhx? And because there are two magnesium Adams on the right, there must be to make me to Madam's on the left in part B. Earn Transferable Credit & Get your Degree, Get access to this video and our entire Q&A library, The Kinetic Molecular Theory: Properties of Gases, Limiting Reactants & Calculating Excess Reactants, LeChatelier's Principle: Disruption and Re-Establishment of Equilibrium, Single-Displacement Reaction: Definition & Examples, Balancing Nuclear Equations & Predicting the Product of a Nuclear Reaction, Assigning Oxidation Numbers to Elements in a Chemical Formula, Collision Theory: Definition & Significance, Synthesis Reaction: Definition, Formula & Examples, Writing and Balancing Combustion Reactions, Dalton's Law of Partial Pressures: Calculating Partial & Total Pressures, Double Displacement Reaction: Definition & Examples, Electron Configurations & the Four Quantum Numbers, Metallic Bonding: The Electron-Sea Model & Why Metals Are Good Electrical Conductors, The Periodic Table: Properties of Groups and Periods, Rate of a Chemical Reaction: Modifying Factors, DSST Principles of Physical Science: Study Guide & Test Prep, Principles of Physical Science: Certificate Program, High School Physical Science: Help and Review, Physical Science for Teachers: Professional Development, General Chemistry Syllabus Resource & Lesson Plans, Organic & Inorganic Compounds Study Guide, Physical Science Curriculum Resource & Lesson Plans, Science 102: Principles of Physical Science, High School Physical Science: Homework Help Resource, High School Physical Science: Tutoring Solution, Biological and Biomedical 2[H3PO4] + … 3 MgS + 2 H 3 PO 4 → Mg 3 (PO 4) 2 + 3 H 2 S. Reaction Information. Repeat steps 3 and 4 until the chemical equation is balanced. Balancing Strategies: In this combination reaction the MgO and H2O come together to from Magnesium hydroxide.That's way it's called a combination reaction. Explanation: #MgO(s) + 2HCl(aq) rarr MgCl_2(aq) + H_2O(l)# Most oxides follow this scheme on treatment with strong acid. Magnesium Oxide - MgO. 2. Question: Magnesium oxide can be made by heating magnesium metal in the presence of oxygen. Write and balance a symbol equation for the reaction in Q2. If there's one oxygen, one die atomic, oxygen, there must be two magnesium oxide. To balance a chemical equation, enter an equation of a chemical reaction and press the Balance button. …, ye pray karoBhaiya aap pray karna taran sis ke liye hum sab bhi unke liye pray kar rahe hain taran di theek ho jayengi bhaiya, પદ સંશા કેવી રીતે દર્શાવાય છે ?Term symbol is represented byA) (S +- 1 }xC) ( 25 + 1 ,B) (2s 1,D} 25 26,.. It's a reversible reaction. Every balanced chemical equation follows law of conservation of mass. ➡ MgO + H2O = Mg (OH)2 Here in the above chemical reaction, when magnisium oxide reacts with water, it forms Magnesium hydroxide. Iron can be transformed to steel (an alloy), which is more resistant to rust. Mg + H3PO4 = Mg3 (PO4)2 + H2 | Chemical reaction and equation Magnesium react with phosphoric acid 3Mg + 2H 3 PO 4 → Mg 3 (PO 4) 2 + 3H 2 [ Check the balance ] I know the answer is MgO. Services, The Activity Series: Predicting Products of Single Displacement Reactions, Working Scholars® Bringing Tuition-Free College to the Community. b. Magnesium oxide (Mg O), or magnesia, is a white hygroscopic solid mineral that occurs naturally as periclase and is a source of magnesium (see also oxide).It has an empirical formula of Mg O and consists of a lattice of Mg 2+ ions and O 2− ions held together by ionic bonding. The balanced equation for the reaction is: 2Mg(s)+O2(g)→2MgO(s) When 10.1 g of Mg are allowed to react with 10.5 g of O2, 10.9g of MgO are collected. ' Therefore, sulphuric acid and magnesium oxide will react with each other to yield magnesium sulphate and water. When the magnesium hydroxide is heated, it will produce magnesium oxide and water. Type of Chemical Reaction: For this reaction we have a combination reaction. © copyright 2003-2021 Study.com. Explain your answer. These reactions can be used to purify synthesized magnesium oxide: Heating solid magnesium will produce both magnesium oxide and magnesium nitride. Examples: Fe, Au, Co, Br, C, O, N, F. Ionic charges are not yet supported and will be ignored. a. Word equation: Magnesium oxide + Water → Magnesium hydroxide. Which is more reactive, magnesium or copper? What are the products when magnesium oxide reacts with hydrochloric acid? Determine the limiting reactant for the reaction. Instructions. The result will be elemental hydrogen (which is naturally diatomic) and magnesium chloride. Thus many procedures will include a step where sandpaper is used to remove the oxide layer from magnesium before use. Use uppercase for the first character in the element and lowercase for the second character. Magnesium (Mg) is weighed in a crucible and then heated to create a reaction with O₂ to produce Magnesium Oxide (MgO). Hence, adding water to the resulting solids and heating them will produce pure magnesium oxide. Magnesium iodide can be prepared from magnesium oxide, magnesium hydroxide, and magnesium carbonate by treatment with hydroiodic acid:. An acid + a metal oxide → salt + water. Explanation: Every balanced chemical equation follows law of conservation of mass.. A balanced chemical equation is defined as the equation in which total number of individual atoms on the reactant side is equal to the total number of individual atoms on product side.. Write a word equation for the reaction of magnesium with oxygen. And then after ever in the products, then I can go back in right coefficients to balance the equation. magnesium oxide + carbon balanced equation This reaction is a combustion and oxidation. {/eq}. a solid that is physically and chemically stable at high temperatures. Write a balanced equation for the reaction of {eq}Mg The formula for magnesium oxide is MgO. [ Check the balance ] Magnesium oxide react with sulfuric acid to produce magnesium sulfate and water. Magnesium oxide can be made by heating magnesium metal in the presence of the oxygen. Magnesium reacts with oxygen to form magnesium oxide: 2 Mg + O 2 → 2 MgO. $H_2SO_4 + MgO \rightarrow MgSO_4 + H_2O$ 1 Answer anor277 Mar 15, 2017 #"Magnesium chloride"# and #"water"#. So the formula from a medium oxide is N g o. MgO + H 2 SO 4 → MgSO 4 + H 2 O. You can use parenthesis () … Iron reacts with oxygen to form iron oxide: 4 Fe + 3 O 2 → 2 Fe 2 O 3. Chemistry Chemical Reactions Chemical Reactions and Equations. To balance a chemical equation, enter an equation of a chemical reaction and press the Balance button. Express your answer as a chemical formula. The balanced chemical equation is: MgO + H2O â†’ Mg (OH)2 The product formed in this reaction is magnesium hydroxide. Similarly, what is the word equation for 2mg o2 2MgO? The balanced chemical equation for the reaction of magnesium oxide with water follows: What is the chemical equation when magnesium reacts with phosphoric acid? Average percent yield of magnesium oxide [This will be done by averaging the % yield of your 2-3 trials] 98.55%; STUDENT LAB DISCUSSION. Balanced Chemical Equation Stoichiometry Chemical equations can be interpreted to describe the relationships occurring in the reaction. Sciences, Culinary Arts and Personal Pure magnesium is a highly reactive metal and will react with oxygen if exposed to the air. Write the balanced chemical equation for this reaction. 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text:
# Point M is the midpoint between points A and B. If A is (-9, 2) and B is (-1,-8) where is M?

Then teach the underlying concepts
Don't copy without citing sources
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#### Explanation

Explain in detail...

#### Explanation:

I want someone to double check my answer

1
Mar 10, 2018

See a solution process below:

#### Explanation:

The formula to find the mid-point of a line segment give the two end points is:

$M = \left(\frac{\textcolor{red}{{x}_{1}} + \textcolor{b l u e}{{x}_{2}}}{2} , \frac{\textcolor{red}{{y}_{1}} + \textcolor{b l u e}{{y}_{2}}}{2}\right)$

Where $M$ is the midpoint and the given points are:

$\left(\textcolor{red}{{x}_{1}} , \textcolor{red}{{y}_{1}}\right)$ and $\left(\textcolor{b l u e}{{x}_{2}} , \textcolor{b l u e}{{y}_{2}}\right)$

Substituting the values from the points in the problem gives:

${M}_{A - B} = \left(\frac{\textcolor{red}{- 9} + \textcolor{b l u e}{- 1}}{2} , \frac{\textcolor{red}{2} + \textcolor{b l u e}{- 8}}{2}\right)$

${M}_{A - B} = \left(\frac{- 10}{2} , \frac{- 6}{2}\right)$

${M}_{A - B} = \left(- 5 , - 3\right)$

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# Walking, Weak first-order transitions, and Complex CFTs II. Two-dimensional Potts model at $Q>4$

### Submission summary

 As Contributors: Slava Rychkov Arxiv Link: https://arxiv.org/abs/1808.04380v2 (pdf) Date submitted: 2018-08-29 02:00 Submitted by: Rychkov, Slava Submitted to: SciPost Physics Academic field: Physics Specialties: High-Energy Physics - Theory Statistical and Soft Matter Physics Approach: Theoretical

### Abstract

We study complex CFTs describing fixed points of the two-dimensional $Q$-state Potts model with $Q>4$. Their existence is closely related to the weak first-order phase transition and walking RG behavior present in the real Potts model at $Q>4$. The Potts model, apart from its own significance, serves as an ideal playground for testing this very general relation. Cluster formulation provides nonperturbative definition for a continuous range of parameter $Q$, while Coulomb gas description and connection to minimal models provide some conformal data of the complex CFTs. We use one and two-loop conformal perturbation theory around complex CFTs to compute various properties of the real walking RG flow. These properties, such as drifting scaling dimensions, appear to be common features of the QFTs with walking RG flows, and can serve as a smoking gun for detecting walking in Monte Carlo simulations. The complex CFTs discussed in this work are perfectly well defined, and can in principle be seen in Monte Carlo simulations with complexified coupling constants. In particular, we predict a pair of $S_5$-symmetric complex CFTs with central charges $c\approx 1.138 \pm 0.021 i$ describing the fixed points of a 5-state dilute Potts model with complexified temperature and vacancy fugacity.

### Ontology / Topics

See full Ontology or Topics database.

###### Current status:
Has been resubmitted

### Submission & Refereeing History

Resubmission 1808.04380v3 on 16 October 2018

Submission 1808.04380v2 on 29 August 2018

## Reports on this Submission

### Anonymous Report 2 on 2018-9-24 (Invited Report)

• Cite as: Anonymous, Report on arXiv:1808.04380v2, delivered 2018-09-24, doi: 10.21468/SciPost.Report.588

### Strengths

the paper is 1. new 2. interesting

none

### Report

see the file attached

none

### Attachment

• validity: top
• significance: high
• originality: high
• clarity: high
• formatting: excellent
• grammar: excellent

### Report 1 by Sylvain Ribault on 2018-9-17 (Invited Report)

• Cite as: Sylvain Ribault, Report on arXiv:1808.04380v2, delivered 2018-09-17, doi: 10.21468/SciPost.Report.580

### Strengths

1- Predicting and checking the nontrivial Im-flip condition for CFT data.

2- Good review of the 2d Potts model.

3- A foray into the oft-neglected 2d CFTs with complex central charges.

### Weaknesses

1- Clarity and precision are sometimes lacking.

### Report

This article demonstrates the phenomenon of walking RG flows, which was proposed in the authors' previous article, in the example of the 2d Potts model. Technically, this draws on results from three areas: the 2d Potts model, general CFT in 2d, and conformal perturbation theory. My knowledge of the first and third areas is limited, and it would be good to have the article reviewed by experts such as the authors of refs. [4] and [32] respectively.

The main reason why I am confident that the article's picture of walking RG flows is correct, is that it provides predictions for relations between conformal dimensions and three-point structure constants: the Im-flip condition of Section 4.2. The authors check this condition in a few nontrivial cases, and I believe that it holds more generally, based on rough calculations.

Other reasons why the article is interesting are a good (but perfectible) review of the 2d Potts model, and the foray into the oft-neglected CFTs with complex central charges.

I believe that the text's clarity could and should be improved in many places. Although the general ideas are clear enough, the technical implementation is sometimes hard to follow. In my suggestions I will first focus on three areas that deserve particular attention, before giving miscellaneous comments on the rest of the text.

NOTE ADDED AFTER VETTING: I am grateful to the authors for their explanations, which greatly helped me write this report.

### Requested changes

(For a better formatted and printable version, see the attached PDF.)

\subsubsection*{Sections 2.4, 2.5 and 2.6}

These Sections review the spectrum of the Potts model. While only some features of this spectrum will be needed later, this review is valuable in itself. I believe that its clarity and presentation should be improved along the following lines:

1- We have no less than 7 different notations for what is essentially the same parameter: $c, Q, e_0, g, u, t, m$. Using several notations is probably inevitable, as this parameter appears in various contexts and approaches. But 7 notations are too many, maybe $Q,c,g$ or $Q,c,t$ could suffice? If many notations are kept, at least there should be a synthetic table of their relations.

2- Similarly, we have two notations $(e,m)$ and $(r,s)$ for the same parameters. The discussion of Section 2.6 on how degenerate characters emerge from various terms in the partition function would be greatly helped by using $(r,s)$ throughout, as degenerate representations correspond to $r,s$ being nonzero integers of the same sign. Or at least, the partition function should be rewritten in terms of $(r,s)$ in Section 2.6.

3- The formulas (2.15) and (2.16) are standard formulas in free bosonic CFTs in the presence of a background charge. The present text gives the misleading impression that these formulas are specific to the Potts model, and gives derivations that are not very enlightening and probably unnecessary. Once we know that we should compute the functional integral (2.13), these formulas can be immediately accepted, with possibly a reference to the textbook by di Francesco et al, Chapter 9.

4- If $e\in 2\mathbb{Z}$ for electric operators (Section 2.4), why is $e\in \mathbb{Z}$ allowed in $Z_c[g,1]$ (2.23)-(2.19)?

5- It is strange that the partition function (2.23) is only partially described in Section 2.5, with one piece being postponed to eq. (2.31). Some restructuring would be welcome.

6- $1$ is not prime: is (2.32) valid for $M=1$?

7- The notation $\mathcal{O}_{e_0+2P,0}$ is confusing, and obscures the fact that this operator is degenerate if $P\geq 0$. A notation of the type $V_{1,P+1}$ would be much better. (I used the letter $V$ in order to avoid the confusion with the non-diagonal operators $\mathcal{O}_{e,m}$.) Similarly, in (2.31) the notation $x_{e_0+2P,0}$ is confusing.

8- When discussing the operator content, in other words when grouping the terms in the partition function into Virasoro characters, it would be good to systematically indicate which contributions come from which terms with which multiplicities. Presenting all the results as a table might be appropriate.

9- Statements like 'does not exist as a primary operator' or 'a scalar operator of such dimension is present, but it's not a primary' or 'do not exist in the spectrum' should be made more precise.
In the presence of negative multiplicities, it is hard to say anything definite about the spectrum: having statements about characters would be more accurate. The appearance of degenerate characters suggests, but does not prove, that degenerate operators exist.
And null vectors are both primary and descendent.

10- In Figure 7, at $Q=2$ there seem to be two degenerate operators of type $(1,2)$ with different dimensions.

11- The operators $\epsilon,\epsilon'$ are actually degenerate at all $Q$, whereas Figure 7 suggests that they are degenerate at $Q=2,3$ only. The indices $(r,s)$ are not just positions in the Kac table: they make sense at all central charges.

\subsubsection*{Section 4 before 4.1}

The authors could be more cautious in declaring that the light fields belong to degenerate conformal families: the characters suggest it, but do not prove it. Character-wise, a degenerate representation coincides with two non-degenerate representations with multiplicities $1$ and $-1$, and we are in a context where negative multiplicities are allowed.

Moreover, fixing three-point functions of degenerate fields at arbitrary central charges using crossing symmetry is not really complicated. The authors' extrapolation from minimal models is a circuitous route, which gives correct results because the correlators are uniquely determined by differential equations and crossing relations, as the authors correctly state. But this holds for all central charges.

Therefore, I recommend the following:

12- Remove the discussion of minimal models, with its odd focus on the unitary models with integer $m$, whereas more general minimal models have two integer parameters. (The mention of minimal models in the article's Abstract could also be removed.)

13- Give the explicit formulas for the structure constants, as this would allow readers to check their subsequent expansions around $c=1$. (See footnote 24.) The natural parameter for writing these structure constants is not $m$, but rather $t$ or $g$. Maybe these formulas could be given in an appendix, which would include all needed structure constants, including those of the spin operator.

\subsubsection*{Section 4.5}

This Section gives arguments for the existence of the real flow, based on the assumption that there exists a pair of complex conjugate CFTs that are moreover related by a RG flow. There are two arguments:

# A first-order argument argument, plus a heuristic argument that higher order corrections should deform the picture without changing it qualitatively.

# An all-orders argument, which is not rigorous because regularization issues are neglected, and which essentially shows the equivalence between the existence of the real flow, and the fixed point coupling constant $g_{FP}$ being pure imaginary.

The all-orders argument does not really establish the existence of the real flow, but it does constitute a nontrivial consistency check. Moreover, it illustrates the properties of correlation functions in complex CFTs.

The logic of the argument, and the technical details, are not clear enough.
Moreover, while interesting, this Section is not crucial to the main results of Section 4.
I recommend that Section 4.5 be either deleted, or clarified. Suggested clarifications include:

14- Removing the misleading sentence 'a formal perturbative argument for the existence of the real theory', and clarifying the sentence after (4.49) as establishing '$X\Leftrightarrow Y$' rather than '$X$ and $Y$'.

15- Explaining the precise meaning of the two relations between the CFTs $\mathcal{C}$ and $\bar{\mathcal{C}}$, at the level of correlation functions: the relation that they are complex conjugates, and the relation via the RG flow.

16- 'regulate' $\to$ 'regularize'.

17- Typo in (4.47): $g_2^k$. Same problem in (4.48).

18- Better explaining the crucial step of going from the first to the second line of (4.47).

19- Clarifying that at each point on the RG flow, there exists only one operator $\Phi$ or $\Phi^*$, which however changes name from $\Phi$ to $\Phi^*$ at some point, and which becomes real in a real CFT.

\subsubsection*{Miscellaneous suggestions}

20- Does eq. (2.4) for the partition function at arbitrary $Q$ reduces to (2.2) when $Q$ is integer?

21- In Subsection 2.2, clarify the statement that 'we will take an intuitive approach to symmetry for non-integer $Q$...'. Does this symmetry for non-integer $Q$ play any role in the subsequent analysis?

22- Typo: 'complex plain'.

23- In Section 3, the statement that 'multiplicities stay real for any $Q$' could be amended to 'multiplicities stay real for any real $Q$'. Thinking about complex $Q$ is indeed quite natural in this context.

24- There is a dual use of the notation $g$ in Section 4.1: the original $g$ from eq. (2.6), which appears again in eq. (4.7), differs from the $g$ in eq. (4.9).

25- In Section 4.1, the description of the Im-flip is confusing. Maybe an equation would be clearer.

26- In the Conclusion, state explicitly what it means to 'make sense' for a Euclidean CFT, and for a Minkowskian CFT.

### Attachment

• validity: top
• significance: top
• originality: top
• clarity: top
• formatting: perfect
• grammar: perfect


text:
# An Infeasible Interior-Point Algorithm with full-Newton Step for Linear Optimization

In this paper we present an infeasible interior-point algorithm for solving linear optimization problems. This algorithm is obtained by modifying the search direction in the algorithm [C. Roos, A full-Newton step ${O}(n)$ infeasible interior-point algorithm for linear optimization, 16(4) 2006, 1110-1136.]. The analysis of our algorithm is much simpler than that of the Roos's algorithm at some places. The iteration bound of the algorithm is as good as the best known iteration bound $O(n\log \frac{1}{\varepsilon})$ for IIPMs.

## Citation

Manuscript. Delft University of Technology. P.O. Box 5031, 2600 GA Delft, The Netherlands. May 2007.


text:
## cPanel: How to install mod_geoip

There are many ways to install mod_geoip in the web but the easiest way to compile it with cPanel server is to compile it through the easyapache.

To get it done, run the following command as root,

cd /var/cpanel/easy/apache/custom_opt_mods/
wget http://docs.cpanel.net/twiki/pub/EasyApache3/CustomMods/custom_opt_mod-mod_geoip.tar.gz
tar -zxf custom_opt_mod-mod_geoip.tar.gz

Next compile it with easyapache,

/scripts/easyapache

Then select the Mod_GeoIP in the Short Options List.


text:
# Implicit newline at the end of each paragraph?

In certain styles of text I define \parindent 0pt in the preamble and write the text in this manner:

paragraph 1\\

paragraph 2\\

and so forth. I wonder if there is anything I can include in the preamble for instance to modify how the end of paragraphs are interpreted so I don't have to include the \\ at the end of each?

Use

\setlength\parskip{\baselineskip}

or perhaps better use

\usepackage{parskip}

and never end a paragraph with \\ it just makes LaTeX generate loads of warnings and poor typesetting.

• You can as well use {\baselineskip plus 0.15\baselineskip minus 0.5\baselineskip} to allow some stretch. – yo' Aug 9 '12 at 22:12
• Yes that's better really (or there's the parskip package for a more complete job) I was was trying to keep it simple. – David Carlisle Aug 9 '12 at 23:02
• The parskip package is exactly what I needed - but the simplicity of this answer is nice too. When I try to compile with @tohecz answer though I get ! LaTeX Error: Missing \begin{document}.. – hatmatrix Aug 10 '12 at 8:38
• @tohecz comment was intended to be the argument to \setlength not a line on its own. – David Carlisle Aug 10 '12 at 8:41


text:
## Volkwein, Stefan

Compute Distance To:
 Author ID: volkwein.stefan Published as: Volkwein, Stefan; Volkwein, S. External Links: MGP
 Documents Indexed: 81 Publications since 1997 1 Contribution as Editor Co-Authors: 64 Co-Authors with 68 Joint Publications 1,388 Co-Co-Authors
all top 5

### Co-Authors

 14 single-authored 8 Kunisch, Karl 5 Mechelli, Luca 4 Hinze, Michael 4 Kahlbacher, Martin 4 Lass, Oliver 3 Hintermüller, Michael 3 Iapichino, Laura 3 Sachs, Ekkehard W. 3 Tröltzsch, Fredi 3 Urban, Karsten 2 Alla, Alessandro 2 Beermann, Dennis 2 Borzì, Alfio 2 Diwoky, Franz 2 Falcone, Maurizio 2 Gubisch, Martin 2 Herzog, Roland 2 Hömberg, Dietmar 2 Leibfritz, Friedemann 2 Tonn, Timo 2 Weiser, Martin 2 Wesche, Andrea 1 Bachmann, Freya 1 Banholzer, Stefan 1 Benner, Peter 1 Bernreuther, Marco 1 Dellnitz, Michael 1 Düring, Bertram 1 Fabrini, Giulia 1 Fuertinger, Doris H. 1 Gänzler, Tobias 1 Gräßle, Carmen 1 Grüne, Lars 1 Jäkle, Christian 1 Jehle, Jonas Siegfried 1 Jüngel, Ansgar 1 Kammann, Eileen 1 Kappel, Franz 1 Keil, Tim 1 Keiper, Winfried 1 Kopacka, Ian 1 Kotanko, Peter 1 Lu, Jianjie 1 Makarov, Eugen 1 Mancini, Roberta 1 Milde, Anja 1 Neitzel, Ira 1 Ohlberger, Mario 1 Peitz, Sebastian 1 Pirkelmann, Simon 1 Posch, C. 1 Rogg, S. 1 Salomon, Julien 1 Scharrer, G. 1 Schindler, Felix 1 Spasov, Yulian 1 Studinger, Alina 1 Trenz, Stefan 1 Ulbrich, Stefan 1 Unterrreiter, A. 1 von Winckel, Gregory 1 Vossen, Georg 1 Xie, Lei 1 Zeeb, Oliver
all top 5

### Serials

 8 Computational Optimization and Applications 5 European Series in Applied and Industrial Mathematics (ESAIM): Mathematical Modelling and Numerical Analysis 4 SIAM Journal on Control and Optimization 4 Optimization Methods & Software 3 Journal of Optimization Theory and Applications 3 SIAM Journal on Scientific Computing 2 Control and Cybernetics 2 SIAM Journal on Numerical Analysis 2 Advances in Computational Mathematics 2 European Series in Applied and Industrial Mathematics (ESAIM): Control, Optimization and Calculus of Variations 2 Mathematical and Computer Modelling of Dynamical Systems 2 PAMM. Proceedings in Applied Mathematics and Mechanics 1 International Journal for Numerical Methods in Fluids 1 Journal of Mathematical Biology 1 Journal of Computational and Applied Mathematics 1 Nonlinear Analysis. Theory, Methods & Applications. Series A: Theory and Methods 1 Numerische Mathematik 1 COMPEL 1 Mathematical and Computer Modelling 1 Journal of Scientific Computing 1 Linear Algebra and its Applications 1 SIAM Review 1 Grazer Mathematische Berichte 1 Numerical Linear Algebra with Applications 1 ZAMM. Zeitschrift für Angewandte Mathematik und Mechanik 1 Discussiones Mathematicae. Differential Inclusions, Control and Optimization 1 SIAM Journal on Applied Dynamical Systems 1 Communications in Mathematical Sciences 1 GAMM-Mitteilungen 1 Numerical Algebra, Control and Optimization 1 Mathematical Control and Related Fields
all top 5

### Fields

 61 Calculus of variations and optimal control; optimization (49-XX) 46 Numerical analysis (65-XX) 41 Partial differential equations (35-XX) 27 Operations research, mathematical programming (90-XX) 14 Systems theory; control (93-XX) 7 Classical thermodynamics, heat transfer (80-XX) 5 Fluid mechanics (76-XX) 5 Optics, electromagnetic theory (78-XX) 2 General and overarching topics; collections (00-XX) 2 Quantum theory (81-XX) 2 Biology and other natural sciences (92-XX) 1 Approximations and expansions (41-XX) 1 Mechanics of deformable solids (74-XX) 1 Game theory, economics, finance, and other social and behavioral sciences (91-XX)

### Citations contained in zbMATH Open

75 Publications have been cited 1,187 times in 650 Documents Cited by Year
Galerkin proper orthogonal decomposition methods for a general equation in fluid dynamics. Zbl 1075.65118
Kunisch, K.; Volkwein, S.
2002
Galerkin proper orthogonal decomposition methods for parabolic problems. Zbl 1005.65112
Kunisch, K.; Volkwein, S.
2001
Control of the Burgers equation by a reduced-order approach using proper orthogonal decomposition. Zbl 0949.93039
Kunisch, K.; Volkwein, S.
1999
Proper orthogonal decomposition surrogate models for nonlinear dynamical systems: error estimates and suboptimal control. Zbl 1079.65533
Hinze, Michael; Volkwein, Stefan
2005
Proper orthogonal decomposition for optimality systems. Zbl 1141.65050
Kunisch, Karl; Volkwein, Stefan
2008
POD a-posteriori error estimates for linear-quadratic optimal control problems. Zbl 1189.49050
Tröltzsch, F.; Volkwein, S.
2009
HJB-POD-based feedback design for the optimal control of evolution problems. Zbl 1058.35061
Kunisch, K.; Volkwein, S.; Xie, L.
2004
Optimal control of a phase-field model using proper orthogonal decomposition. Zbl 1007.49019
Volkwein, S.
2001
Analysis of instantaneous control for the Burgers equation. Zbl 1022.49001
Hinze, M.; Volkwein, S.
2002
Error estimates for abstract linear-quadratic optimal control problems using proper orthogonal decomposition. Zbl 1191.49040
Hinze, M.; Volkwein, S.
2008
Optimal snapshot location for computing POD basis functions. Zbl 1193.65113
Kunisch, Karl; Volkwein, Stefan
2010
Model order reduction for PDE constrained optimization. Zbl 1327.49043
Benner, Peter; Sachs, Ekkehard; Volkwein, Stefan
2014
Control of laser surface hardening by a reduced-order approach using proper orthogonal decomposition. Zbl 1044.49028
Hömberg, D.; Volkwein, S.
2003
Application of the augmented Lagrangian-SQP method to optimal control problems for the stationary Burgers equation. Zbl 0974.49020
Volkwein, S.
2000
The SQP method for control constrained optimal control of the Burgers equation. Zbl 1001.49035
Tröltzsch, Fredi; Volkwein, Stefan
2001
A primal-dual active set strategy for optimal boundary control of a nonlinear reaction-diffusion system. Zbl 1090.49024
Griesse, R.; Volkwein, S.
2005
Distributed control problems for the Burgers equation. Zbl 0976.49001
Volkwein, S.
2001
Galerkin proper orthogonal decomposition methods for parameter dependent elliptic systems. Zbl 1156.35020
Kahlbacher, Martin; Volkwein, Stefan
2007
POD-Galerkin approximations in PDE-constrained optimization. Zbl 1205.35011
Sachs, Ekkehard W.; Volkwein, Stefan
2010
A posteriori error estimation for semilinear parabolic optimal control problems with application to model reduction by POD. Zbl 1282.49021
Kammann, Eileen; Tröltzsch, Fredi; Volkwein, Stefan
2013
Reduced order output feedback control design for PDE systems using proper orthogonal decomposition and nonlinear semidefinite programming. Zbl 1149.90183
Leibfritz, F.; Volkwein, S.
2006
Comparison of the reduced-basis and POD a posteriori error estimators for an elliptic linear-quadratic optimal control problem. Zbl 1302.49045
Tonn, Timo; Urban, K.; Volkwein, S.
2011
POD Galerkin schemes for nonlinear elliptic-parabolic systems. Zbl 1272.49060
Lass, O.; Volkwein, S.
2013
Formulation and numerical solution of finite-level quantum optimal control problems. Zbl 1143.65048
Borzì, A.; Salomon, J.; Volkwein, S.
2008
A globalized Newton method for the accurate solution of a dipole quantum control problem. Zbl 1206.35211
Von Winckel, G.; Borzì, A.; Volkwein, S.
2009
Error analysis for POD approximations of infinite horizon problems via the dynamic programming approach. Zbl 1378.49025
Alla, A.; Falcone, M.; Volkwein, S.
2017
Multiobjective PDE-constrained optimization using the reduced-basis method. Zbl 1386.35068
Iapichino, L.; Ulbrich, S.; Volkwein, Stefan
2017
Greedy sampling using nonlinear optimization. Zbl 1322.65070
Urban, Karsten; Volkwein, Stefan; Zeeb, Oliver
2014
Lagrange-SQP techniques for the control constrained optimal boundary control for the Burgers equation. Zbl 1077.49024
Volkwein, S.
2003
Dynamical systems-based optimal control of incompressible fluids. Zbl 1081.76016
Hintermüller, Michael; Kunisch, Karl; Spasov, Yulian; Volkwein, Stefan
2004
POD a-posteriori error analysis for optimal control problems with mixed control-state constraints. Zbl 1302.49039
Gubisch, Martin; Volkwein, Stefan
2014
Optimal and suboptimal control of partial differential equations: Augmented Lagrange-SQP methods and reduced-order modeling with proper orthogonal decomposition. Zbl 1005.49029
Volkwein, Stefan
2001
POD a-posteriori error based inexact SQP method for bilinear elliptic optimal control problems. Zbl 1272.49059
Kahlbacher, Martin; Volkwein, Stefan
2012
Numerical analysis of POD a-posteriori error estimation for optimal control. Zbl 1275.49050
Studinger, Alina; Volkwein, Stefan
2013
Parametric sensitivity analysis for optimal boundary control of a 3D reaction-diffusion system. Zbl 1104.49033
Griesse, Roland; Volkwein, Stefan
2006
Mesh-independence for an augmented Lagrangian-SQP method in Hilbert spaces. Zbl 0945.49024
Volkwein, S.
2000
Second-order conditions for boundary control problems of the Burgers equation. Zbl 1001.93033
Volkwein, Stefan
2001
Optimal control of parameter-dependent convection-diffusion problems around rigid bodies. Zbl 1216.49006
Tonn, Timo; Urban, Karsten; Volkwein, Stefan
2010
Parameter identification for nonlinear elliptic-parabolic systems with application in lithium-ion battery modeling. Zbl 1342.49055
Lass, Oliver; Volkwein, Stefan
2015
Nonlinear conjugate gradient methods for the optimal control of laser surface hardening. Zbl 1062.49034
Volkwein, Stefan
2004
Affine invariant convergence analysis for inexact augmented Lagrangian-SQP methods. Zbl 1029.49026
Volkwein, S.; Weiser, M.
2002
Nonlinear boundary control for the heat equation utilizing proper orthogonal decomposition. Zbl 0999.49019
Diwoky, Franz; Volkwein, Stefan
2001
Set-oriented multiobjective optimal control of PDEs using proper orthogonal decomposition. Zbl 1418.65165
Beermann, Dennis; Dellnitz, Michael; Peitz, Sebastian; Volkwein, Stefan
2018
Reduced-order multiobjective optimal control of semilinear parabolic problems. Zbl 1352.65166
Iapichino, Laura; Trenz, Stefan; Volkwein, Stefan
2016
Asymptotic stability of POD based model predictive control for a semilinear parabolic PDE. Zbl 1329.93072
Alla, Alessandro; Volkwein, Stefan
2015
The reduced basis method applied to transport equations of a lithium-ion battery. Zbl 1358.78076
Volkwein, Stefan; Wesche, Andrea
2013
Adaptive POD basis computation for parametrized nonlinear systems using optimal snapshot location. Zbl 1302.49040
Lass, Oliver; Volkwein, Stefan
2014
Estimation of regularization parameters in elliptic optimal control problems by POD model reduction. Zbl 1189.93036
Kahlbacher, Martin; Volkwein, Stefan
2009
Augmented Lagrange-SQP methods with Lipschitz-continuous Lagrange multiplier updates. Zbl 1027.49027
Sachs, E.; Volkwein, S.
2002
POD-based economic model predictive control for heat-convection phenomena. Zbl 1425.93133
Mechelli, Luca; Volkwein, Stefan
2018
Model reduction techniques with a-posteriori error analysis for linear-quadratic optimal control problems. Zbl 1254.49018
Vossen, Georg; Volkwein, Stefan
2012
Admittance identification from point-wise sound pressure measurements using reduced-order modelling. Zbl 1205.49032
Volkwein, S.
2010
A-posteriori error estimation of discrete POD models for PDE-constrained optimal control. Zbl 06861101
Gubisch, Martin; Neitzel, Ira; Volkwein, Stefan
2017
An inverse scattering problem for the time-dependent Maxwell equations: nonlinear optimization and model-order reduction. Zbl 1313.78032
Mancini, Roberta; Volkwein, Stefan
2013
Optimality system POD and a-posteriori error analysis for linear-quadratic problems. Zbl 1318.49067
Volkwein, Stefan
2011
Fast solution techniques in constrained optimal boundary control of the semilinear heat equation. Zbl 1239.49039
Hintermüller, M.; Volkwein, S.; Diwoky, F.
2007
Optimal control of the stationary quantum drift-diffusion model. Zbl 1152.35096
Unterrreiter, A.; Volkwein, S.
2007
Estimation of diffusion coefficients in a scalar Ginzburg-Landau equation by using model reduction. Zbl 1157.65473
Kahlbacher, M.; Volkwein, S.
2008
Augmented Lagrangian-SQP techniques and their approximations. Zbl 0913.49018
Kunisch, K.; Volkwein, S.
1997
Optimal EPO dosing in hemodialysis patients using a non-linear model predictive control approach. Zbl 1430.92039
Rogg, S.; Fuertinger, D. H.; Volkwein, S.; Kappel, F.; Kotanko, P.
2019
Coupling MPC and HJB for the computation of POD-based feedback laws. Zbl 07136779
Fabrini, Giulia; Falcone, Maurizio; Volkwein, Stefan
2018
POD-based economic optimal control of heat-convection phenomena. Zbl 1416.49026
Mechelli, Luca; Volkwein, Stefan
2018
Numerical feedback controller design for PDE systems using model reduction: techniques and case studies. Zbl 1226.93069
Leibfritz, Friedemann; Volkwein, Stefan
2007
SQP methods for parameter identification problems arising in hyperthermia. Zbl 1113.65067
Gänzler, T.; Volkwein, S.; Weiser, M.
2006
Mesh-independence of Lagrange-SQP methods with Lipschitz-continuous Lagrange multiplier updates. Zbl 1079.90166
Volkwein, S.
2002
Mesh-independence and preconditioning for solving parabolic control problems with mixed control-state constraints. Zbl 1167.49027
Hintermüller, Michael; Kopacka, Ian; Volkwein, Stefan
2009
Sequential quadratic programming method for volatility estimation in option pricing. Zbl 1159.91389
Düring, B.; Jüngel, A.; Volkwein, S.
2009
Boundary control of the Burgers equation: Optimality conditions and reduced-order approach. Zbl 1024.49024
Volkwein, Stefan
2002
POD-based mixed-integer optimal control of the heat equation. Zbl 1430.49023
Bachmann, Freya; Beermann, Dennis; Lu, Jianjie; Volkwein, Stefan
2019
POD-based multiobjective optimal control of time-variant heat phenomena. Zbl 1425.90145
Banholzer, Stefan; Makarov, Eugen; Volkwein, Stefan
2018
Condition number of the stiffness matrix arising in POD Galerkin schemes for dynamical systems. Zbl 1354.65199
Volkwein, Stefan
2004
Reduced-order modeling (ROM) for simulation and optimization. Powerful algorithms as key enablers for scientific computing. Zbl 1402.65010
2018
Model order reduction by proper orthogonal decomposition. Zbl 1467.35021
Gräßle, Carmen; Hinze, Michael; Volkwein, Stefan
2021
Performance estimates for economic model predictive control and their application in proper orthogonal decomposition-based implementations. Zbl 1471.93084
Grüne, Lars; Mechelli, Luca; Pirkelmann, Simon; Volkwein, Stefan
2021
A non-conforming dual approach for adaptive trust-region reduced basis approximation of PDE-constrained parameter optimization. Zbl 07405597
Keil, Tim; Mechelli, Luca; Ohlberger, Mario; Schindler, Felix; Volkwein, Stefan
2021
Model order reduction by proper orthogonal decomposition. Zbl 1467.35021
Gräßle, Carmen; Hinze, Michael; Volkwein, Stefan
2021
Performance estimates for economic model predictive control and their application in proper orthogonal decomposition-based implementations. Zbl 1471.93084
Grüne, Lars; Mechelli, Luca; Pirkelmann, Simon; Volkwein, Stefan
2021
A non-conforming dual approach for adaptive trust-region reduced basis approximation of PDE-constrained parameter optimization. Zbl 07405597
Keil, Tim; Mechelli, Luca; Ohlberger, Mario; Schindler, Felix; Volkwein, Stefan
2021
Optimal EPO dosing in hemodialysis patients using a non-linear model predictive control approach. Zbl 1430.92039
Rogg, S.; Fuertinger, D. H.; Volkwein, S.; Kappel, F.; Kotanko, P.
2019
POD-based mixed-integer optimal control of the heat equation. Zbl 1430.49023
Bachmann, Freya; Beermann, Dennis; Lu, Jianjie; Volkwein, Stefan
2019
Set-oriented multiobjective optimal control of PDEs using proper orthogonal decomposition. Zbl 1418.65165
Beermann, Dennis; Dellnitz, Michael; Peitz, Sebastian; Volkwein, Stefan
2018
POD-based economic model predictive control for heat-convection phenomena. Zbl 1425.93133
Mechelli, Luca; Volkwein, Stefan
2018
Coupling MPC and HJB for the computation of POD-based feedback laws. Zbl 07136779
Fabrini, Giulia; Falcone, Maurizio; Volkwein, Stefan
2018
POD-based economic optimal control of heat-convection phenomena. Zbl 1416.49026
Mechelli, Luca; Volkwein, Stefan
2018
POD-based multiobjective optimal control of time-variant heat phenomena. Zbl 1425.90145
Banholzer, Stefan; Makarov, Eugen; Volkwein, Stefan
2018
Reduced-order modeling (ROM) for simulation and optimization. Powerful algorithms as key enablers for scientific computing. Zbl 1402.65010
2018
Error analysis for POD approximations of infinite horizon problems via the dynamic programming approach. Zbl 1378.49025
Alla, A.; Falcone, M.; Volkwein, S.
2017
Multiobjective PDE-constrained optimization using the reduced-basis method. Zbl 1386.35068
Iapichino, L.; Ulbrich, S.; Volkwein, Stefan
2017
A-posteriori error estimation of discrete POD models for PDE-constrained optimal control. Zbl 06861101
Gubisch, Martin; Neitzel, Ira; Volkwein, Stefan
2017
Reduced-order multiobjective optimal control of semilinear parabolic problems. Zbl 1352.65166
Iapichino, Laura; Trenz, Stefan; Volkwein, Stefan
2016
Parameter identification for nonlinear elliptic-parabolic systems with application in lithium-ion battery modeling. Zbl 1342.49055
Lass, Oliver; Volkwein, Stefan
2015
Asymptotic stability of POD based model predictive control for a semilinear parabolic PDE. Zbl 1329.93072
Alla, Alessandro; Volkwein, Stefan
2015
Model order reduction for PDE constrained optimization. Zbl 1327.49043
Benner, Peter; Sachs, Ekkehard; Volkwein, Stefan
2014
Greedy sampling using nonlinear optimization. Zbl 1322.65070
Urban, Karsten; Volkwein, Stefan; Zeeb, Oliver
2014
POD a-posteriori error analysis for optimal control problems with mixed control-state constraints. Zbl 1302.49039
Gubisch, Martin; Volkwein, Stefan
2014
Adaptive POD basis computation for parametrized nonlinear systems using optimal snapshot location. Zbl 1302.49040
Lass, Oliver; Volkwein, Stefan
2014
A posteriori error estimation for semilinear parabolic optimal control problems with application to model reduction by POD. Zbl 1282.49021
Kammann, Eileen; Tröltzsch, Fredi; Volkwein, Stefan
2013
POD Galerkin schemes for nonlinear elliptic-parabolic systems. Zbl 1272.49060
Lass, O.; Volkwein, S.
2013
Numerical analysis of POD a-posteriori error estimation for optimal control. Zbl 1275.49050
Studinger, Alina; Volkwein, Stefan
2013
The reduced basis method applied to transport equations of a lithium-ion battery. Zbl 1358.78076
Volkwein, Stefan; Wesche, Andrea
2013
An inverse scattering problem for the time-dependent Maxwell equations: nonlinear optimization and model-order reduction. Zbl 1313.78032
Mancini, Roberta; Volkwein, Stefan
2013
POD a-posteriori error based inexact SQP method for bilinear elliptic optimal control problems. Zbl 1272.49059
Kahlbacher, Martin; Volkwein, Stefan
2012
Model reduction techniques with a-posteriori error analysis for linear-quadratic optimal control problems. Zbl 1254.49018
Vossen, Georg; Volkwein, Stefan
2012
Comparison of the reduced-basis and POD a posteriori error estimators for an elliptic linear-quadratic optimal control problem. Zbl 1302.49045
Tonn, Timo; Urban, K.; Volkwein, S.
2011
Optimality system POD and a-posteriori error analysis for linear-quadratic problems. Zbl 1318.49067
Volkwein, Stefan
2011
Optimal snapshot location for computing POD basis functions. Zbl 1193.65113
Kunisch, Karl; Volkwein, Stefan
2010
POD-Galerkin approximations in PDE-constrained optimization. Zbl 1205.35011
Sachs, Ekkehard W.; Volkwein, Stefan
2010
Optimal control of parameter-dependent convection-diffusion problems around rigid bodies. Zbl 1216.49006
Tonn, Timo; Urban, Karsten; Volkwein, Stefan
2010
Admittance identification from point-wise sound pressure measurements using reduced-order modelling. Zbl 1205.49032
Volkwein, S.
2010
POD a-posteriori error estimates for linear-quadratic optimal control problems. Zbl 1189.49050
Tröltzsch, F.; Volkwein, S.
2009
A globalized Newton method for the accurate solution of a dipole quantum control problem. Zbl 1206.35211
Von Winckel, G.; Borzì, A.; Volkwein, S.
2009
Estimation of regularization parameters in elliptic optimal control problems by POD model reduction. Zbl 1189.93036
Kahlbacher, Martin; Volkwein, Stefan
2009
Mesh-independence and preconditioning for solving parabolic control problems with mixed control-state constraints. Zbl 1167.49027
Hintermüller, Michael; Kopacka, Ian; Volkwein, Stefan
2009
Sequential quadratic programming method for volatility estimation in option pricing. Zbl 1159.91389
Düring, B.; Jüngel, A.; Volkwein, S.
2009
Proper orthogonal decomposition for optimality systems. Zbl 1141.65050
Kunisch, Karl; Volkwein, Stefan
2008
Error estimates for abstract linear-quadratic optimal control problems using proper orthogonal decomposition. Zbl 1191.49040
Hinze, M.; Volkwein, S.
2008
Formulation and numerical solution of finite-level quantum optimal control problems. Zbl 1143.65048
Borzì, A.; Salomon, J.; Volkwein, S.
2008
Estimation of diffusion coefficients in a scalar Ginzburg-Landau equation by using model reduction. Zbl 1157.65473
Kahlbacher, M.; Volkwein, S.
2008
Galerkin proper orthogonal decomposition methods for parameter dependent elliptic systems. Zbl 1156.35020
Kahlbacher, Martin; Volkwein, Stefan
2007
Fast solution techniques in constrained optimal boundary control of the semilinear heat equation. Zbl 1239.49039
Hintermüller, M.; Volkwein, S.; Diwoky, F.
2007
Optimal control of the stationary quantum drift-diffusion model. Zbl 1152.35096
Unterrreiter, A.; Volkwein, S.
2007
Numerical feedback controller design for PDE systems using model reduction: techniques and case studies. Zbl 1226.93069
Leibfritz, Friedemann; Volkwein, Stefan
2007
Reduced order output feedback control design for PDE systems using proper orthogonal decomposition and nonlinear semidefinite programming. Zbl 1149.90183
Leibfritz, F.; Volkwein, S.
2006
Parametric sensitivity analysis for optimal boundary control of a 3D reaction-diffusion system. Zbl 1104.49033
Griesse, Roland; Volkwein, Stefan
2006
SQP methods for parameter identification problems arising in hyperthermia. Zbl 1113.65067
Gänzler, T.; Volkwein, S.; Weiser, M.
2006
Proper orthogonal decomposition surrogate models for nonlinear dynamical systems: error estimates and suboptimal control. Zbl 1079.65533
Hinze, Michael; Volkwein, Stefan
2005
A primal-dual active set strategy for optimal boundary control of a nonlinear reaction-diffusion system. Zbl 1090.49024
Griesse, R.; Volkwein, S.
2005
HJB-POD-based feedback design for the optimal control of evolution problems. Zbl 1058.35061
Kunisch, K.; Volkwein, S.; Xie, L.
2004
Dynamical systems-based optimal control of incompressible fluids. Zbl 1081.76016
Hintermüller, Michael; Kunisch, Karl; Spasov, Yulian; Volkwein, Stefan
2004
Nonlinear conjugate gradient methods for the optimal control of laser surface hardening. Zbl 1062.49034
Volkwein, Stefan
2004
Condition number of the stiffness matrix arising in POD Galerkin schemes for dynamical systems. Zbl 1354.65199
Volkwein, Stefan
2004
Control of laser surface hardening by a reduced-order approach using proper orthogonal decomposition. Zbl 1044.49028
Hömberg, D.; Volkwein, S.
2003
Lagrange-SQP techniques for the control constrained optimal boundary control for the Burgers equation. Zbl 1077.49024
Volkwein, S.
2003
Galerkin proper orthogonal decomposition methods for a general equation in fluid dynamics. Zbl 1075.65118
Kunisch, K.; Volkwein, S.
2002
Analysis of instantaneous control for the Burgers equation. Zbl 1022.49001
Hinze, M.; Volkwein, S.
2002
Affine invariant convergence analysis for inexact augmented Lagrangian-SQP methods. Zbl 1029.49026
Volkwein, S.; Weiser, M.
2002
Augmented Lagrange-SQP methods with Lipschitz-continuous Lagrange multiplier updates. Zbl 1027.49027
Sachs, E.; Volkwein, S.
2002
Mesh-independence of Lagrange-SQP methods with Lipschitz-continuous Lagrange multiplier updates. Zbl 1079.90166
Volkwein, S.
2002
Boundary control of the Burgers equation: Optimality conditions and reduced-order approach. Zbl 1024.49024
Volkwein, Stefan
2002
Galerkin proper orthogonal decomposition methods for parabolic problems. Zbl 1005.65112
Kunisch, K.; Volkwein, S.
2001
Optimal control of a phase-field model using proper orthogonal decomposition. Zbl 1007.49019
Volkwein, S.
2001
The SQP method for control constrained optimal control of the Burgers equation. Zbl 1001.49035
Tröltzsch, Fredi; Volkwein, Stefan
2001
Distributed control problems for the Burgers equation. Zbl 0976.49001
Volkwein, S.
2001
Optimal and suboptimal control of partial differential equations: Augmented Lagrange-SQP methods and reduced-order modeling with proper orthogonal decomposition. Zbl 1005.49029
Volkwein, Stefan
2001
Second-order conditions for boundary control problems of the Burgers equation. Zbl 1001.93033
Volkwein, Stefan
2001
Nonlinear boundary control for the heat equation utilizing proper orthogonal decomposition. Zbl 0999.49019
Diwoky, Franz; Volkwein, Stefan
2001
Application of the augmented Lagrangian-SQP method to optimal control problems for the stationary Burgers equation. Zbl 0974.49020
Volkwein, S.
2000
Mesh-independence for an augmented Lagrangian-SQP method in Hilbert spaces. Zbl 0945.49024
Volkwein, S.
2000
Control of the Burgers equation by a reduced-order approach using proper orthogonal decomposition. Zbl 0949.93039
Kunisch, K.; Volkwein, S.
1999
Augmented Lagrangian-SQP techniques and their approximations. Zbl 0913.49018
Kunisch, K.; Volkwein, S.
1997
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### Cited by 977 Authors

 49 Luo, Zhendong 32 Volkwein, Stefan 26 Rozza, Gianluigi 23 Kunisch, Karl 16 Iliescu, Traian 15 Benner, Peter 13 Karasözen, Bülent 13 Navon, Ionel Michael 13 Tröltzsch, Fredi 12 Teng, Fei 11 Singler, John R. 11 Stabile, Giovanni 10 Borzì, Alfio 10 Efendiev, Yalchin R. 10 Gugercin, Serkan 10 Manzoni, Andrea 10 Willcox, Karen E. 9 Alla, Alessandro 9 Haasdonk, Bernard 9 Hinze, Michael 9 Li, Hong 9 Ohlberger, Mario 9 Patera, Anthony T. 9 Quarteroni, Alfio M. 9 Uzunca, Murat 9 Wang, Zhu 8 Chaturantabut, Saifon 8 Shen, Chunyu 7 Chen, Jing 7 Chung, Tsz Shun Eric 7 San, Omer 6 Borggaard, Jeff T. 6 Grepl, Martin A. 6 Veroy, Karen 6 Xie, Zhenghui 6 Zhou, Yanjie 5 Amsallem, David 5 Beattie, Christopher A. 5 Ciaramella, Gabriele 5 Fang, Fangxin 5 Gao, Anna 5 Gildin, Eduardo 5 Kalise, Dante 5 Karatzas, Efthymios N. 5 Peitz, Sebastian 5 Pinnau, René 5 Rebholz, Leo G. 5 Schneier, Michael 5 Sun, Ping 5 Tian, Lixin 5 Yücel, Hamdullah 4 Ballarin, Francesco 4 Barone, Matthew F. 4 Bordas, Stéphane Pierre Alain 4 Breiten, Tobias 4 Choi, Youngsoo 4 Falcone, Maurizio 4 Gunzburger, Max D. 4 Heinkenschloss, Matthias 4 Herzog, Roland 4 Hintermüller, Michael 4 Hömberg, Dietmar 4 Jiang, Lijian 4 Kalashnikova, Irina 4 Kärcher, Mark 4 Kerfriden, Pierre 4 Küçükseyhan, Tuğba 4 Lass, Oliver 4 Lee, Hyung-Chun 4 Maday, Yvon 4 Mola, Andrea 4 Nguyen, Ngoc Cuong 4 Nouy, Anthony 4 Pain, Christopher C. 4 Perotto, Simona 4 Piao, Guangri 4 Reese, Stefanie 4 Rubino, Samuele 4 Ştefănescu, Răzvan 4 Stoll, Martin 4 Ulbrich, Stefan 4 Weiser, Martin 3 Akman, Tuğba 3 Benosman, Mouhacine 3 Chen, Yanlai 3 Di, Zhenhua 3 Dihlmann, Markus A. 3 Dolgov, Sergey V. 3 Du, Juan 3 Fareed, Hiba 3 Farhat, Charbel H. 3 Feng, Lihong 3 Gao, Junqiang 3 Ghattas, Omar N. 3 Grüne, Lars 3 Hamdouni, Aziz 3 Herty, Michael Matthias 3 Hijazi, Saddam 3 Hoang, K. C. 3 Huang, Ting-Zhu ...and 877 more Authors
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### Cited in 34 Fields

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# scipy.signal.ZerosPolesGain.dt¶

property ZerosPolesGain.dt

Return the sampling time of the system, None for lti systems.


text:
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Of the following, which is the best approximation of the per

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Of the following, which is the best approximation of the per [#permalink]  26 Aug 2018, 03:22
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Of the following, which is the best approximation of the percent increase in the national health expenditure per capita from 1981 to 1982 ?

(A) 35%

(B) 30%

(C) 20%

(D) 10%

(E) 5%
[Reveal] Spoiler: OA

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Re: Of the following, which is the best approximation of the per [#permalink]  26 Aug 2018, 06:43
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From the per capita graph in 1981 the amount is approximately 380 and in 1982 the amount was approximately 420.
% increase = $$\frac{420-380}{420} * 100$$ = 9.5% which is approximately 10%
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Re: Of the following, which is the best approximation of the per [#permalink]  21 Feb 2019, 21:05
amorphous wrote:
From the per capita graph in 1981 the amount is approximately 380 and in 1982 the amount was approximately 420.
% increase = $$\frac{420-380}{420} * 100$$ = 9.5% which is approximately 10%

we should divide by 420 or 380.
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Re: Of the following, which is the best approximation of the per [#permalink]  28 Feb 2019, 21:29
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AE wrote:
amorphous wrote:
From the per capita graph in 1981 the amount is approximately 380 and in 1982 the amount was approximately 420.
% increase = $$\frac{420-380}{420} * 100$$ = 9.5% which is approximately 10%

we should divide by 420 or 380.

We divide by 420, the original number.

If you think about it, 420 - 380 = 40, and that's the actual increase that occured.

So we're left with $$\frac{40}{420}$$, which tells us what fraction of the original number, 420, the increase, 40, is. So $$\frac{40}{420}$$ is approximately equal to $$\frac{9.5}{100}$$, or 9.5%. So, the increase of 40 is equal to an increase of 9.5% of our original number.
Re: Of the following, which is the best approximation of the per   [#permalink] 28 Feb 2019, 21:29
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## Classification with All Features

The first experiment undertaken with the HeLa data and the available feature sets was to see how well the classes could be discriminated using all 84 features (Zernike, Haralick, and ad hoc). As part of this endeavor, several classification trials were completed to determine the number of hidden nodes that provided the best performance. BPNNs with 5, 10, 15, 20, and 30 hidden nodes were trained and tested. The average correct classification rate for each of these networks is summarized in Table 3.3. It was decided to use 20 hidden nodes because classifier performance improved up to that point and then plateaued. To maintain consistency among the experiments described below, all BPNNs were configured with 20 hidden nodes.

Table 3.3: Network performance on test samples for various numbers of hidden nodes and all features (Zernike, Haralick, ad hoc).
Hidden Nodes Classification Rate
(mean 95% CI)
5
10
15
20
30

The performance of a BPNN with 20 hidden nodes, using all 84 features is summarized in Table 3.4. These results were obtained using the highest network output value to classify each sample. Results were then obtained using the best threshold'' technique described in Section 3.2.7, and are summarized in Table 3.5. Briefly, a threshold was systematically identified for each BPNN using the stop data set before running the test data through the network. A valid classification was assigned to each test sample only if there was one and only one network output above the threshold, otherwise, the sample was considered unknown''. The threshold approach to classification is included because it has application to the reliable identification of single cells. By applying a threshold to the outputs of the BPNN and making an assignment of class only when a single output achieves that threshold, one has more confidence in the resulting classification. If the goal of a particular experiment is to reliably identify single cells as belonging to a particular class, it may be preferable to make a classification of unknown'' rather than to misclassify a cell. In such a case, thresholding the BPNN outputs is one solution.

 DNA ER Giantin GPP130 LAMP2 Mito. Nucleolin Actin TfR Tubulin True Output of the Classifier Classification DNA ER Giant. GPP LAMP Mito. Nucle. Actin TfR Tubul. 99% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% 86% 3% 0% 0% 5% 0% 0% 0% 5% 0% 0% 77% 0% 1% 2% 0% 1% 0% 0% 0% 78% 2% 0% 2% 0% 1% 0% 0% 1% 3% 2% 73% 1% 2% 0% 1% 0% 9% 2% 0% 4% 77% 0% 0% 2% 6% 2% 0% 1% 2% 1% 0% 94% 0% 0% 0% 0% 0% 0% 0% 0% 3% 0% 91% 0% 6% 0% 5% 3% 1% 3% 0% 5% 55% 5% 0% 5% 0% 0% 1% 7% 1% 4% 5% 77%

The criterion used to identify a good threshold for the outputs of a BPNN is to maximize the sum of the squares of the recall and accuracy of the classifier using a particular threshold. The accuracy of a classifier is the fraction of all classification attempts (total samples minus the number of unknowns) that are successful. The recall of that classifier, on the other hand, is the fraction of all samples that are classified correctly. For example, a classifier that assigns classes to a small fraction of all samples but does so correctly will have high accuracy, but low recall. Furthermore, a classifier with perfect accuracy and perfect recall will operate at a point farther from the origin of a recall vs. accuracy plot than any other possible classifier (see Figure 3.6). The accuracy2 + recall2 criterion was therefore chosen to find the threshold that causes a particular classifier to operate as far from the origin of the recall vs. accuracy plot as possible.

The average performance of the non-thresholded classifier is acceptable (81% overall), but not entirely satisfactory for some pairs of classes. Two pairs that are frequently confused by the classifier are giantin/GPP130 and LAMP2/transferrin receptor. This result is not unexpected, however, as these are some of the same pairs that were intentionally included to create such confusion. Because the major instances of confusion can be explained biologically, they are not terribly troubling. First of all, both giantin and GPP130 reside in the same organelle (the Golgi), although apparently they do not colocalize entirely. Two possible explanations for this are: 1) they are in different subcompartments of the Golgi, and 2) one of the two proteins does completely co-localize with the other, but the second protein also resides in one or more Golgi subcompartments by itself (i.e., its localization is more extensive). The features must be able to capture such subtle differences between the two patterns.

The second set of significantly confused patterns are LAMP2 and transferrin receptor. Although these two proteins do not reside in the same compartments, their localization patterns tend to be similar (subjectively). Both of these proteins are found in vesicles throughout the cytoplasm (punctate), and tend to be concentrated in a compartment near the nucleus. Again, the features are able to capture enough information about the differences between these two visually similar patterns to distinguish them most of the time.

Finding a threshold for the network outputs and requiring that only one output exceed that threshold turns out to provide only a 5% increase in the average classification rate while also causing between 1% (DNA) and 32% (LAMP2) of the samples from any one class to be placed into the unknown category (Table 3.5). This performance increase, while not large, may none the less be useful for those experiments where correct identification of single cells is a top priority.

 DNA ER Giantin GPP130 LAMP2 Mito. Nucleolin Actin TfR Tubulin True Output of the Classifier Classification DNA ER Giant. GPP LAMP Mito. Nucle. Actin TfR Tubul. Unk. 99% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% (100%) 0% 76% 0% 0% 0% 2% 0% 0% 0% 3% 19% (94%) 0% 0% 70% 14% 0% 1% 0% 0% 1% 0% 14% (81%) 0% 0% 13% 70% 0% 0% 1% 0% 1% 0% 14% (82%) 0% 0% 2% 0% 55% 1% 1% 0% 9% 0% 32% (80%) 0% 7% 2% 0% 2% 68% 0% 0% 1% 5% 15% (80%) 0% 0% 0% 2% 0% 0% 90% 0% 0% 0% 7% (97%) 0% 0% 0% 0% 0% 1% 0% 86% 0% 3% 9% (95%) 0% 3% 0% 0% 17% 0% 0% 3% 45% 2% 29% (63%) 0% 1% 0% 0% 0% 4% 0% 1% 3% 61% 29% (87%)

In order to compare the BPNN to another classifier, all 84 features were used as inputs to a kNN classifier. Table 3.6 summarizes the performance of the 10 kNN trials (see Section 3.2.8 for details). The overall performance of the kNN classifier is not as good as either of the BPNNs (thresholded or non-thresholded outputs). While the kNN classifier produces similar performance for the DNA, giantin, and ER classes as compared to the BPNNs, it is worse at identifying most classes and is much worse at identifying the mitochondrial patterns correctly. The most common misclassifications for mitochondria are ER and Tubulin. From a biological perspective, this is expected (the ER and mitochondrial patterns were intended to be confusing) as all three patterns tend to have peri-nuclear concentrations but are also dispersed away from the nucleus. The BPNN classifiers are able to avoid this mistake, however. Even though the overall kNN performance is not significantly worse than the non-thresholded BPNN (77% vs. 81%), the inability of the kNN classifier to recognize mitochondrial and transferrin receptor patterns makes it a less desirable choice as a classifier.

 DNA ER Giantin GPP130 LAMP2 Mito. Nucleolin Actin TfR Tubulin True Output of the Classifier Classification DNA ER Giant. GPP LAMP Mito. Nucle. Actin TfR Tubul. Unk. 99% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% (99%) 0% 84% 0% 0% 3% 2% 0% 0% 0% 6% 4% (88%) 0% 1% 69% 14% 1% 1% 0% 0% 0% 0% 13% (79%) 0% 1% 16% 65% 4% 0% 1% 0% 2% 0% 12% (74%) 0% 1% 1% 3% 67% 1% 1% 0% 7% 1% 18% (81%) 0% 12% 0% 0% 2% 49% 0% 0% 5% 15% 17% (59%) 2% 0% 1% 2% 8% 0% 80% 0% 2% 0% 6% (85%) 0% 0% 0% 0% 0% 1% 0% 72% 0% 12% 14% (84%) 0% 7% 0% 1% 25% 5% 0% 4% 29% 7% 21% (37%) 0% 8% 0% 2% 2% 8% 0% 2% 4% 62% 13% (71%)


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# The cost function in the Weighted Bipartite Matching Problem (a.k.a the Assignment Problem)

In the definition of this problem, the weight/cost function generally takes value in $\mathbb{Z}$ (or sometimes $\mathbb{Q}$). This is what I observed from some books (e.g. "Combinatorial Optimization: Polyhedra and Efficiency" by Alexander Schrijver) and some implementations I found on the web.

My question is: How does a real-valued cost function affect the solution, such as the Hungarian method?

It still works when you take real valued weights, as long as you are careful. Such discussion is usually avoided because you need to be careful about what you mean by a real number and how you are representing it, plus the fact that you don't actually have the ability to use any real number as a weight. Because of these caveats, people tend to stick to rational numbers or integers, since the problem doesn't gain anything from going to the real numbers, as you can scale the weights by a large multiplicative factor to move them very far apart and then "round" to some close rational number. Since there are a finite number of numbers involved, there is infact guarenteed to be an $\alpha\in\mathbb{N}$ such that replacing $r_i$ with $\lceil\alpha r_i\rceil$ doesn't change the solution to the problem
The Hungarian algorithm (HA) still works efficiently for real weights; see Remark 3.2.12 on p.129 of West's "Introduction to Graph Theory", 2nd edn., for example. In brief, suppose we run the algorithm on a weighted $K_{n,n}$. Recall that the HA maintains a partial matching $M$ at each step; once that matching becomes full-size, the algorithm is done. The size of $M$ never decreases in any iteration of the HA, and each iteration that it does not strictly increase, the number of vertices in one partite set searched by the augmenting-path algorithm does increase, which means that $M$ has to get bigger at least once every $n$ iterations of the HA. So the HA terminates in at most $n^2$ steps. This argument is purely combinatorial and works just as well for real weights.


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 January 24th, 2018, 10:01 AM #1 Newbie   Joined: Aug 2016 From: Pakistan Posts: 9 Thanks: 0 Problem A closed box made of wood 1 cm thick, has external dimensions 20 cm by 15 cm by 10 cm. find the mass of the empty box if 1 cubic decimeter of wood weighs 1 kg?
 January 24th, 2018, 11:09 AM #2 Global Moderator   Joined: Dec 2006 Posts: 19,281 Thanks: 1680 If the box has internal dimensions 18 cm by 13 cm by 8 cm, the volume of the wood is (20×15×10 - 18×13×8) cm³ = 1.128 dm³. Does that help?
 January 24th, 2018, 11:12 AM #3 Math Team   Joined: Jan 2015 From: Alabama Posts: 3,261 Thanks: 893 The simplest way to do this is to first calculate the volume of a 20 by 15 by 10 cm solid: 10(15)(20)= 3000 cubic cm. Then subtract off the 19 by 14 by 9 cm interior: 9(14)(19)=2394 cubic cm. The wood making up the box has volume 3000- 2394= 606 cubic cm. A "decimeter' is 10 cm so a cubic decimeter is $10^3= 1000$ cubic cm so 606 cubic cm is 606/1000= 0.606 cubic decimeter.

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# 2018 Annual Meeting of the Society for Mathematical Biology & the Japanese Society for Mathematical Biology

8-12 July 2018
Australia/Sydney timezone

## Stopping waves: geometric analysis of coupled bursters in an asymmetric excitation field

9 Jul 2018, 15:40
20m
New Law School/--022 (University of Sydney)

### New Law School/--022

#### University of Sydney

60
Oral Presentation Biochemistry and Cell Biology

### Speaker

Dr Iulia Martina Bulai (University of Padova,Italy)

### Description

Bursting is a type of electrical activity seen in many neurons and endocrine cells where episodes of action potential firing are interspersed by silent phases. Pancreatic $\beta$-cells show so-called square-wave bursting when stimulated by glucose, which causes ${\text Ca^{2+}}$ oscillations and pulsatile insulin secretion. $\beta$-cells are electrically coupled within pancreatic islets, and excitation waves are observed to propagate through the $\beta$-cell population. When the islet is exposed to a glucose gradient, so that some cells would be active also when uncoupled while others would be below the activity threshold and thus silent, ${\text Ca^{2+}}$ waves propagate only partly through the islet and stop approximately where the glucose concentration is at the threshold for cellular activity. Simulations of existing mathematical models of coupled $\beta$-cells produce waves that propagate too far into the region of "silent" cells, compared to experiments, unless unrealistic model assumptions are imposed. Here, we investigate why $\beta$-cell models fail to reproduce the experimentally observed wave properties and tend to synchronize the $\beta$-cell population too easily, by using a prototypical polynomial bursting model and slow/fast bifurcation analysis. Our analyses indicate how to modify the model so that the excitation waves stop at the border between "active" and "silent" cells. We verify this property by simulations using such a modified model for a chain, and for a cubic cluster, of coupled $\beta$-cells. Furthermore, we show how our one- and two-parameter bifurcation analyses allow us to predict where the simulated waves stop, for both the original model and the modified version. Our results indicate the geometrical structure that biophysical $\beta$-cell models should have to possess biologically realistic wave and synchronization properties.

### Primary authors

Dr Iulia Martina Bulai (University of Padova,Italy) Prof. Morten Gram Pedersen (University of Padova, Italy)

### Presentation Materials

There are no materials yet.