Tag: optimization

Feed: SAS Blogs. Author: Rick Wicklin. I previously wrote about one way to solve the partition problem in SAS. In the partition problem, you divide (or partition) a set of N items into two groups of size k and N-k such that the sum of the items' weights is the same in each group. For example, if the weights of six items are X = {0.4, 1.0, 1.2, 1.7, 2.6, 2.7} and k=3, you can put the weights {0.4, 1.7, 2.7} in one group and the weights {1.0, 1.2, 2.6} in the other group. Both groups contain 4.8 ... Read More

Feed: Recent Announcements. Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), extends Automatic Table Optimization to modify column compression encodings. This new capability maintains the optimal compression encodings in Amazon Redshift database tables on an ongoing basis to achieve high performance and reduce the storage utilization. By using automation to manage column compression, customers can ensure that column compression encodings are always optimized in order to get the best possible performance. A compression encoding specifies the type of compression that is applied to data values in a column as rows are added to a table. Choosing the right ... Read More

Feed: Journey to AI Blog; Author: Alex Fleischer; Today, machine learning (ML), artificial intelligence (AI) and decision optimization (DO) are not just buzzwords you read in the press, but urgent requirements for any company that fears disruption and wants to do pragmatic analysis in order to make better decisions with their data. Data is the next natural resource but as with any resource, it’s not worth much until it helps you show results. Investment in AI can help accelerate and enhance those results. A recent technical validation report by the Enterprise Strategy Group (ESG) mentioned that “improving operational efficiency” is ... Read More

Feed: R-bloggers. Author: Di Cook. #=================================================================## Finance and Insurance Engineering using R # by Sang-Heon Lee & Hosam Ki## https://kiandlee.blogspot.com#—————————————————————–## Portfolio Optimization using ROI NEOS#=================================================================#graphics.off()  # clear all graphsrm(list = ls()) # remove all files from your workspacelibrary(quadprogXT)      # solveQPXTlibrary(ROI)             # ROI_solvelibrary(ROI.plugin.neos) # NEOS#—————————————————–# Michuad dataset   #—————————————————–    setwd(“D:/SHLEE/blog/rneos/michuad”)    # dataset in Michaud and Michaud (2007) appendix    df.data – read.csv(‘mu_sd_corr_michaud.csv’)    head(df.data)    # mean, std, correlation    mu   – as.vector(df.data[,1])    sd   – as.vector(df.data[,2])    corr – as.matrix(df.data[,–c(1,2)])    # number of asset    nvar – length(mu)    var.name – colnames(df.data[,–c(1,2)])    # convert correlation to covariance matrix    cov – diag(sd)%*%corr%*%diag(sd)#—————————————————–# Portfolio optimization using solveQPXT#—————————————————–    n.er – 100  # number of EF points    rset – seq(min(mu),max(mu),length=n.er+2)    rset – rset[2:n.er+1]    # given returns and unknown std and weight    port1.ret – rset    port1.std – rset*0    port1.wgt – matrix(0,n.er,nvar)    # ith portfolio problem setting    i = 10;    Dmat – 2*cov    dvec – rep(0,nvar) #c(0,0)    Amat – t(rbind(t(rep(1,nvar)),t(mu),diag(nvar)))    bvec – c(1,rset[i],rep(0,nvar))    # mean-variance optimization    m–solveQPXT(Dmat,dvec,Amat,bvec,meq=2,factorized=FALSE)    # output    port1.std[i]  – sqrt(m$value)    port1.wgt[i,] – t(m$solution)#—————————————————–# Portfolio optimization using ROI.neos.plugin#—————————————————–    #——————–    # min w’*cov*w      # s.t.     #    sum(w) = 1    #    w*r       #    0     #——————–    # objective function to minimize    obj – Q_objective(Q = 2*cov)    # 2 linear constraints    const – L_constraint(         L   = rbind(rep(1, nvar), mu),         dir = c(“==”,“),         rhs = c(1, rset[10]))    # lower and upper bounds    lb_ub – V_bound(lb = rep(0, nvar),                     ub = rep(1, nvar))    # create optimization problem    op – OP( objective   = obj,               constraints = const,               bounds      = lb_ub)    res_scip  – ROI_solve(op, solver = “neos”, method = “scip”,                            email=“your email address”)    res_mosek – ROI_solve(op, solver = “neos”, method = “mosek”,                           email=“your email address”)       res_cplex – ROI_solve(op, solver = “neos”, method = “cplex “,                           email=“your email address”)   #—————————————————–# Comparisons#—————————————————–    # function value    cat(paste(“nobj val : solveQPXT   =”, m$value, “n”,              ”         NEOS(scip)  =”, res_scip$objval, “n”,              ”         NEOS(mosek) =”, res_mosek$objval, “n”,              ”         NEOS(cplex) =”, res_cplex$objval, “n”))    # weight comparisons : solveQPXT versus NEOS(mosek)    output_mosek – cbind(res_mosek$solution,  port1.wgt[i,],                          res_mosek$solution – port1.wgt[i,])    colnames(output_mosek) – c(“NEOS(mosek)”, “solveQPXT”, “diff”)    print(output_mosek) ... Read More

You probably already know that you should avoid using ORs in your WHERE clause because it makes it difficult or impossible for the query optimizer to come up with a good execution plan for the query that runs fully indexed. But what can you actually do about it when you functionally really need that OR and you need the query to run fast? Well, as it turns out, it is sometimes possible to have your cake and eat it! Here is a technique our MySQL support specialists often use to improve query performance in cases where using ORs is necessary.Consider ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Saajan Sharma. Data science is one of the hottest topics in the market nowadays. It is one of those industries that has revolutionized the world. It associates two chief technologies, big data and artificial intelligence, and utilizes them to examine and process datasets. It also uses machine learning, which helps to strengthen artificial intelligence. Data science has thoroughly improved and modernized every industry it has touched, including marketing, finance, social media, SEO, etc. If you wish to excel in your profession, there is a high chance you will have to use data ... Read More

While most experienced DBAs already know this, it is seldom that an example this clear comes up to illustrate the point. That alone makes this story worth sharing, as a lesson to never use a sub-SELECT if you can achieve the same thing with a JOIN. Not heeding that warning is likely to line you up for pain further down the line, long after you forgot writing a query with a sub-SELECT.So with that intro out of the way, let’s dig into the actual story. Recently we were engaged to provide MySQL support for a client who was experiencing performance ... Read More

Our MySQL consultants get asked this a lot, so we have decided to put together a list of best practices for writing performance SQL queries. While database implementations differ, the underlying mathematics they are based around apply to all of them, and consequently these best practices broadly apply across most database management systems. In this article we discuss a list of best practices to help you write queries that perform well and avoid writing queries that end up being unoptimizable. Avoid using sub-SELECTs when you can use JOINs. If you have to use a sub-SELECT, consider using a temporary table ... Read More

Feed: AWS Public Sector Blog. Author: Christopher Goodman. Weather and climate data are important building blocks for digital farming solutions and critical for informing the numerous strategic and operational decisions that farmers must make daily. From selecting the right seed and crop production purchases, spray location and timing, or determining when to plant and harvest, having access to timely and accurate weather information is vital. However, most weather and climate data products available don’t reflect field-specific conditions and are therefore not directly relevant to the spatial and temporal scales at which farmers make their decisions. This is a particularly challenging ... Read More

Feed: MariaDB Knowledge Base Article Feed. Author: . NOT NULL range scan optimization enables the optimizer to construct range scans from NOT NULL conditions that it was able to infer from the WHERE clause. Description A basic (but a bit artificial) example: create table items ( price decimal(8,2), weight decimal(8,2), ... index(weight) ); -- Find items that cost more than 1000 $currency_units per kg: set optimizer_switch='not_null_range_scan=on'; explain select * from items where items.price > items.weight / 1000; The WHERE condition in this form cannot be used for range scans. However, one can infer that it will reject rows that NULL ... Read More