Step 1: Sketch constraint functions (Eqs 3.38b and 3.38c) and side constraints (Eq. 3.38d) on the design plane of two design variables, x1 and x2. Step 2: Identify the feasible region, in which any point in the region satisfies the constraints. Step 3a: Sketch iso-lines of the objec...
Linear regression models are fitted in RevoScaleR using the rxLinMod function. Like other RevoScaleR functions, rxLinMod uses an updating algorithm to compute the regression model. The R object returned by rxLinMod includes the estimated model coefficients and the call used to generate the mo...
Solve the linear program. Get x = linprog(f,A,b,Aeq,beq,lb,ub) Optimal solution found. x = 2×1 0.1875 1.2500 Linear Program Using the 'interior-point' Algorithm Copy Code Copy Command Solve a linear program using the 'interior-point' algorithm. For this example, use these line...
These functions and structures are declared in the header file linear.h. You can see train.c and predict.c for examples showing how to use them. We define LIBLINEAR_VERSION and declare extern int liblinear_version; in linear.h, so you can check the version number. Function: model* train...
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cjlin1/liblinear LIBLINEAR is a simple package for solving large-scale regularized linear classification, regression and outlier detection. It currently supports - L2-regularized logistic regression/L2-loss support vector classification/L1-loss support vector classification - L1-regularized L2-loss ...
In this work, we introduce a relaxed version of the Bellman operator for q-functions and prove that it is still a monotone contraction mapping with a unique fixed point. In the spirit of the LP approach, we exploit the new operator to build a relaxed linear program (RLP). Compared to ...
Bozga, Iosif and Konecný’s FLATA tool [13] considers affine functions in arbitrary dimension. However, it is restricted to affine functions with finite monoids; in our one-dimensional case this would correspond to limiting oneself to counter-like functions of the form. ...
In this paper, we introduce the notion of a self-regular function. Such a function is strongly convex and smooth coercive on its domain, the positive real axis. We show that any such function induces a so-called self-regular proximity function and a corresponding search direction for primal-...