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#include <cstdlib>

#include <iostream>

#include <SuiteSparseQR.hpp>

int main(int argc, char* argv[]) {
  cholmod_common common;
  cholmod_sparse *A;
  cholmod_dense *x, *b, *residual;

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(defpackage :python-interpreter
  (:use :common-lisp :cffi)
  (:export #:run-python))

(in-package :python-interpreter)

(define-foreign-library libpython (:unix "libpython3.10.so"))

(use-foreign-library libpython)

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Common Lisp vs. Python 3.11 vs. Cython vs. C++ Performance for Simulations

Introduction

We compare the median run times of the agent-based simulation code discussed in the article titled The Bitter Truth: Python 3.11 vs Cython vs C++ Performance for Simulations published on Dec 19, 2022 against a corresponding Common Lisp implementation.

The Common Lisp implementation is (emphatically) written in non-idiomatic Lisp closely following the reference implementations, with some type declarations and compiled using SBCL.

The fastest implementation turns out to be Lisp, with a slight advantage over C++ (see table below).

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from abc import ABC, abstractmethod
from typing import Optional

import numpy as np

from diffusion_maps import diffusion_maps
from mapping import Mapping


class Embedding(ABC):

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diff --git a/libview/ev-view.c b/libview/ev-view.c
index 1ffbf16..079cfd0 100644
--- a/libview/ev-view.c
+++ b/libview/ev-view.c
@@ -8616,7 +8616,7 @@ ev_view_change_page (EvView *view,
        gint x, y;
 
        view->current_page = new_page;
-       view->pending_scroll = SCROLL_TO_PAGE_POSITION;
+       view->pending_scroll = SCROLL_TO_KEEP_POSITION;

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"""Numerically reparameterize a curve by its arc-length.

"""

from typing import Optional

import numpy as np
import scipy
import scipy.interpolate

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#ifndef DOUBLE2_H
#define DOUBLE2_H

#include <cmath>

#include <ostream>
#include <initializer_list>

struct double2 {
  union {

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import numpy as np
import mdtraj as md
import tqdm


def compute_distance_matrix(trajectory: md.Trajectory) -> np.ndarray:
    """Compute distance matrix.

    """
    topology = trajectory.topology

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#!/bin/sh

if [ $# -ge 1 ]; then
    bibtex_file=$1
else
    bibtex_file="bibliography.bib"
fi

awk -F "[{,]" '/^@/ { print $2 }' $bibtex_file \
| while read reference; do

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from joblib import Parallel, delayed
import numpy as np

n = 10000                       # Number of experiments
dt: float = 1e-5                # Time step length


def integrate_until_exit(random_seed):
    np.random.seed(random_seed)