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Objective: Medical over- and underutilization are central problems that stand in the way of delivering optimal health care. As a result, one important question is how people decide to take action, versus not, when it comes to their health. The present article proposes and validates a new measure that captures the extent to which individuals are "medical maximizers" who are predisposed to seek health care even for minor problems, versus "medical minimizers" who prefer to avoid medical intervention unless it is necessary.

True if fit successful, otherwise false. A fit is considered successful if the minimizer succeeded in finding the minimum. It could happen that subsequent operations like error analysis (e.g. Minos) failed. In that case the status can be still true if the original minimization algorithm succeeded in finding the minimum. One can query in that case the minimizer return status using Status(). It is responsibility to the Minimizer class to tag a found minimum as valid or not and to produce also a status code.

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This method calls scipy.optimize.basinhopping using thedefault arguments. The default minimizer is BFGS, but sincelmfit supports parameter bounds for all minimizers, the user canchoose any of the solvers present in scipy.optimize.minimize.

Here is the list of all possible minimizers and their respective methods (algorithms) that can be instantiated: The name shown below can be used to create them. More documentation can be found in the respective class

Since visible pores play a role in how smooth and refined your skin looks, TimeWise Pore Minimizer goes beyond skin texture to tighten the look of pores. With pores cleared of buildup and dead skin cells by microdermabrasion refine, pore minimizer works immediately to reduce their appearance. And the results get better over time.

In linguistics, a minimizer is a word or phrase that denotes a very small quantity which is used to reinforce negation. For example, red cent is a minimizer in the sentence "I'm not paying him a red cent" (meaning, "I'm not paying him any money").

Minimizers are usually analysed as a subclass of negative polarity items, and are often limited to negative contexts.[1] For example, statements like "I paid him a red cent" or "I care a wit" would be considered unacceptable. In English and other languages, minimizers constitute the largest and most productive class of negative polarity items.[2]

The term minimizer was coined by linguist Dwight Bolinger in his 1972 book Degree Words, where he described them as "partially stereotyped equivalents of any".[3][4] The phenomenon had previously been remarked upon by other scholars as far back as August Friedrich Pott in 1859.[5]

Quirk et al. use the term in their 1985 A Comprehensive Grammar of the English Language, classifying minimizers as a subclass of "downtoners" (alongside "approximators", "compromisers", and "diminishers"). Unusually, they include in this category adverbs like barely and hardly which themselves encode negation.[6]

Minimizers are a highly productive class, and new examples can be readily formed from a variety of domains. Early surveys of minimizers across a range of living and dead languages found that some recurring categories included:[10]

Some minimizers are limited to very specific, fixed idiomatic verb phrases (e.g. "move a muscle", "lift a finger", "sleep a wink"), whereas others are highly versatile, such as the semantically bleached shit:

Minimizers are one linguistic element which may develop over time into a marker of sentential negation.[13] For example, negation in French is usually marked with the pre-verbal particle ne and the negative marker pas, as in Je ne sais pas ("I don't know"). pas derives from the Latin passum ("step"), hence "ne... pas" derives from a construction meaning "not a step". In early French, pas could be interchanged with other minimizer nouns such as goutte ("drop") or mie ("crumb"). Similar developments have occurred in other Romance languages. In this way, minimizers have been implicated in Jespersen's cycle, in that a language with pre-verbal negation may develop toward obligatory pre- and post-verbal negation by the grammaticalization of a minimizer which initially is used optionally for emphasis or some other pragmatic purpose.[14]

A particular class of English minimizers based on vulgar or profane language have been observed to have a distinctive property. Like other minimizers, they can appear in non-affirmative contexts with a meaning of "anything", but they can also be used in affirmative contexts, where they seem to take on the meaning "nothing". For example, the following pair of sentences have identical meanings:

If an array of functions is passed to the minify option, the minimizerOptions must also be an array.The function index in the minify array corresponds to the options object with the same index in the minimizerOptions array.

Allows you to override the default minimizer by providing a different one or more customized TerserPlugin instances. Starting with webpack 5.87.0 falsy values can be used to conditionally disable specific minimizers.

Tells webpack to determine used exports for each module. This depends on optimization.providedExports. Information collected by optimization.usedExports is used by other optimizations or code generation i.e. exports are not generated for unused exports, export names are mangled to single char identifiers when all usages are compatible.Dead code elimination in minimizers will benefit from this and can remove unused exports.

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Take the minimizer; clearly, $0\leq x_i\leq a_i$ for all $i$. In particular, if $a_i=0$, then clearly $x_i=0$, and we can get rid of those coordinates. Now we assume $a_i>0$, and it is easy to see that $x_i\in(0,a_i)$.

Taking derivative with respect to $x_i$, we get$$ \frac{x_i}{b}=\frac{s_i(a_i-x_i)}c.$$where $b=\|x\|$ and $c=\|a-x\|_S$. Denoting $t=b/c\in[0,+\infty]$, we arrive at$$ s_i(a_i-x_i)=tx_i, \quad\text{or}\quad x_i=\frac{s_ia_i}{t+s_i},$$so the minimizer lies on a fixed curve (parametrized by $t$).

I know that the minimizer of $\arg \min_{\mathbb{y}} \sum_{i = 1}^{N} \left\| \mathbf{y} - \mathbf{x}_{i} \right\|_2^2$ is the mean of the set.

I also can calculate it per component, so I can calculate $d$ times the mean of $N$ scalars.

Ternary content addressable memories (TCAMs) are becoming very popular due to the simple-to-design IP lookup units included in high-speed routers; they are fast and simple to manage, and they provide a one-clock lookup solution. However, a major drawback of TCAM-based IP lookup schemes lies in their high power consumption. Thus, the rapid increase of routing tables inevitably deteriorates TCAM power efficiency. Although on-chip TCAM minimizers aim for the TCAM power efficiency in a fast time and at a small memory amount, the minimizers are not efficient in a large scale prefix table. In this paper, we present a hash-based on-chip TCAM minimization for a power-and throughput-efficient IP lookup. In a hash-based TCAM minimization (HTM), we convert prefixes into keys and merge keys with a fast hash lookup in an O(nW) complexity, where n is the number of prefixes and W is the number of IP bits. Additionally, by building a forest of merging trees and choosing a subset among them, we can achieve a higher minimization ratio. The simulation with two routing tables shows that our HPM scheme uses 8.6 and 4.0 times fewer computation time and memory, compared to a contemporary on-chip minimizer.

In many estimation problems, e.g. linear and logistic regression, we wish tominimize an unknown objective given only unbiased samples of the objectivefunction. Furthermore, we aim to achieve this using as few samples as possible.In the absence of computational constraints, the minimizer of a sample averageof observed data -- commonly referred to as either the empirical risk minimizer(ERM) or the $M$-estimator -- is widely regarded as the estimation strategy ofchoice due to its desirable statistical convergence properties. Our goal inthis work is to perform as well as the ERM, on every problem, while minimizingthe use of computational resources such as running time and space usage. We provide a simple streaming algorithm which, under standard regularityassumptions on the underlying problem, enjoys the following properties: * The algorithm can be implemented in linear time with a single pass of theobserved data, using space linear in the size of a single sample. * The algorithm achieves the same statistical rate of convergence as theempirical risk minimizer on every problem, even considering constant factors. * The algorithm's performance depends on the initial error at a rate thatdecreases super-polynomially. * The algorithm is easily parallelizable. Moreover, we quantify the (finite-sample) rate at which the algorithm becomescompetitive with the ERM. 006ab0faaa